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book_idtitleauthordescription
01Business Book 1Author 29Negotiation tactics and leadership strategies ...
12Children Book 2Author 21A curious cat explores the city and learns abo...
23Business Book 3Author 19Negotiation tactics and leadership strategies ...
34Business Book 4Author 11How startups scale products and build strong t...
45Children Book 5Author 36A curious cat explores the city and learns abo...
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "books_df", "summary": "{\n \"name\": \"books_df\",\n \"rows\": 200,\n \"fields\": [\n {\n \"column\": \"book_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 57,\n \"min\": 1,\n \"max\": 200,\n \"num_unique_values\": 200,\n \"samples\": [\n 96,\n 16,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"Science Fiction Book 96\",\n \"Mystery Book 16\",\n \"Children Book 31\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"author\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 54,\n \"samples\": [\n \"Author 8\",\n \"Author 20\",\n \"Author 41\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"description\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"Negotiation tactics and leadership strategies for managers.\",\n \"A young hero discovers a hidden kingdom and must defeat a dark sorcerer.\",\n \"Learn to manage anxiety with simple routines and mindset shifts.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "genres = []\n", "scores = []\n", "\n", "for txt in books_df[\"title\"] + \" | \" + books_df[\"description\"]:\n", " g, s = classify_genre(txt)\n", " genres.append(g)\n", " scores.append(s)\n", "\n", "books_df[\"genre\"] = genres\n", "books_df[\"genre_confidence\"] = scores\n", "\n", "books_df[[\"book_id\", \"title\", \"genre\", \"genre_confidence\"]].head(10)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "68CjyXivy7-a", "outputId": "c2213220-fe3a-482d-cec3-8535e643c70e" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " book_id title genre genre_confidence\n", "0 1 Business Book 1 Business 0.959846\n", "1 2 Children Book 2 Children 0.785824\n", "2 3 Business Book 3 Business 0.940125\n", "3 4 Business Book 4 Business 0.919759\n", "4 5 Children Book 5 Children 0.736310\n", "5 6 Children Book 6 Children 0.777509\n", "6 7 History Book 7 History 0.966959\n", "7 8 Children Book 8 Children 0.798004\n", "8 9 History Book 9 History 0.953918\n", "9 10 Fantasy Book 10 Fantasy 0.534217" ], "text/html": [ "\n", "
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book_idtitlegenregenre_confidence
01Business Book 1Business0.959846
12Children Book 2Children0.785824
23Business Book 3Business0.940125
34Business Book 4Business0.919759
45Children Book 5Children0.736310
56Children Book 6Children0.777509
67History Book 7History0.966959
78Children Book 8Children0.798004
89History Book 9History0.953918
910Fantasy Book 10Fantasy0.534217
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"books_df[[\\\"book_id\\\", \\\"title\\\", \\\"genre\\\", \\\"genre_confidence\\\"]]\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"book_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 1,\n \"max\": 10,\n \"num_unique_values\": 10,\n \"samples\": [\n 9,\n 2,\n 6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"History Book 9\",\n \"Children Book 2\",\n \"Children Book 6\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Children\",\n \"Fantasy\",\n \"Business\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre_confidence\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.13849529638695116,\n \"min\": 0.5342167019844055,\n \"max\": 0.9669588208198547,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.9539180397987366,\n 0.7858237028121948,\n 0.777508556842804\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "embed_model = SentenceTransformer(\"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\")\n", "\n", "books_df[\"text\"] = (\n", " \"Title: \" + books_df[\"title\"] +\n", " \" | Author: \" + books_df[\"author\"] +\n", " \" | Genre: \" + books_df[\"genre\"] +\n", " \" | Description: \" + books_df[\"description\"]\n", ")\n", "\n", "book_embeddings = embed_model.encode(\n", " books_df[\"text\"].tolist(),\n", " batch_size=64,\n", " show_progress_bar=True,\n", " convert_to_numpy=True,\n", " normalize_embeddings=True\n", ").astype(\"float32\")\n", "\n", "dim = book_embeddings.shape[1]\n", "index = faiss.IndexFlatIP(dim) # cosine similarity (لأننا normalized)\n", "index.add(book_embeddings)\n", "\n", "print(\"Embeddings:\", book_embeddings.shape)\n", "print(\"FAISS size:\", index.ntotal)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 557, "referenced_widgets": [ "98365767600942b8b227c55a486ed58e", "7e9bc63779c2449b831ce5b2491d7423", "38843d938a9341df85efdc8bfa278481", "33162e15e706410eaf86e991ef7a888e", "be33e12da08e4e4caa5429910b5f3fea", "dc0ad544cd224cc785316f8574ddb09e", "56681e2ecee64567b0d0ee9fc045851d", "b155bea209b44c26858d713dca398677", "2300f5ce0d64448d8e427ef3fdb949d6", "73d566d3959a4a4f9545ceb03a8d225b", "dcf8052a8dc9444cb4a07e137d6a6bc0", "9c26dc083f1644c4801acbd9987299c5", "3a7adfa2fad24359a53a3ee57253942f", "f1706e7b38d14db2b215411325d25693", "cf0f375c7c1444708c3bf986aaebaa9f", "975579b9ce3c47459db4e41b579cb323", "66577f47297d4dd692062447c92ce2bd", "0d097cd3db1f4805b32338aef1a5d5cc", 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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "interactions_df", "summary": "{\n \"name\": \"interactions_df\",\n \"rows\": 1900,\n \"fields\": [\n {\n \"column\": \"user_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14,\n \"min\": 1,\n \"max\": 50,\n \"num_unique_values\": 50,\n \"samples\": [\n 14,\n 40,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 57,\n \"min\": 1,\n \"max\": 200,\n \"num_unique_values\": 200,\n \"samples\": [\n 164,\n 186,\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"event_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"like\",\n \"view\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 30,\n \"samples\": [\n \"2026-03-01T17:14:45.542474\",\n \"2026-03-09T17:14:45.542474\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "code", "source": [ "EVENT_W = {\"view\": 1.0, \"like\": 3.0}\n", "bookid_to_idx = {bid: i for i, bid in enumerate(books_df[\"book_id\"].tolist())}\n", "\n", "def user_interest_ratios(user_id, interactions_df):\n", " u = interactions_df[interactions_df.user_id == user_id].merge(\n", " books_df[[\"book_id\", \"genre\"]], on=\"book_id\", how=\"left\"\n", " )\n", " if u.empty:\n", " return {g: 1/len(GENRES) for g in GENRES}\n", "\n", " u[\"w\"] = u[\"event_type\"].map(EVENT_W).fillna(0.0)\n", " s = u.groupby(\"genre\")[\"w\"].sum().reindex(GENRES, fill_value=0.0)\n", "\n", " total = s.sum()\n", " if total == 0:\n", " return {g: 1/len(GENRES) for g in GENRES}\n", "\n", " return (s / total).to_dict()\n", "\n", "def build_user_vector(user_id, interactions_df):\n", " u = interactions_df[interactions_df.user_id == user_id]\n", " if u.empty:\n", " return None, set()\n", "\n", " vecs, weights, seen = [], [], set()\n", " for _, row in u.iterrows():\n", " bid = int(row[\"book_id\"])\n", " ev = row[\"event_type\"]\n", " if bid not in bookid_to_idx:\n", " continue\n", " w = EVENT_W.get(ev, 0.0)\n", " if w == 0:\n", " continue\n", " vecs.append(book_embeddings[bookid_to_idx[bid]])\n", " weights.append(w)\n", " seen.add(bid)\n", "\n", " if not vecs:\n", " return None, seen\n", "\n", " vecs = np.array(vecs)\n", " weights = np.array(weights).reshape(-1, 1)\n", "\n", " user_vec = np.sum(vecs * weights, axis=0) / (np.sum(np.abs(weights)) + 1e-9)\n", " user_vec = user_vec / (np.linalg.norm(user_vec) + 1e-9)\n", " return user_vec.astype(\"float32\"), seen\n" ], "metadata": { "id": "0ZorIjJwy8E9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "uid = 1\n", "\n", "print(\"Interest ratios:\", user_interest_ratios(uid, interactions_df))\n", "\n", "feed = build_mixed_feed(uid, interactions_df, feed_size=50)\n", "feed.head(10)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 400 }, "id": "HjFP21bmy8H5", "outputId": "0ad5ced2-8fe0-4823-fb94-b5923a3dc8c7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Interest ratios: {'Fantasy': 0.1111111111111111, 'Romance': 0.07407407407407407, 'Mystery': 0.2037037037037037, 'Science Fiction': 0.2222222222222222, 'Self-Help': 0.05555555555555555, 'History': 0.05555555555555555, 'Business': 0.05555555555555555, 'Children': 0.1111111111111111, 'Horror': 0.0, 'Poetry': 0.1111111111111111}\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " book_id title author genre score\n", "0 109 Mystery Book 109 Author 24 Mystery 0.799200\n", "1 169 Mystery Book 169 Author 58 Mystery 0.791255\n", "2 93 Mystery Book 93 Author 59 Mystery 0.789383\n", "3 37 Fantasy Book 37 Author 7 Fantasy 0.768045\n", "4 151 Science Fiction Book 151 Author 20 Science Fiction 0.758007\n", "5 79 Fantasy Book 79 Author 49 Fantasy 0.757395\n", "6 96 Science Fiction Book 96 Author 7 Science Fiction 0.753472\n", "7 97 Science Fiction Book 97 Author 33 Science Fiction 0.752389\n", "8 134 Horror Book 134 Author 24 Mystery 0.750338\n", "9 166 Science Fiction Book 166 Author 13 Science Fiction 0.749133" ], "text/html": [ "\n", "
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book_idtitleauthorgenrescore
0109Mystery Book 109Author 24Mystery0.799200
1169Mystery Book 169Author 58Mystery0.791255
293Mystery Book 93Author 59Mystery0.789383
337Fantasy Book 37Author 7Fantasy0.768045
4151Science Fiction Book 151Author 20Science Fiction0.758007
579Fantasy Book 79Author 49Fantasy0.757395
696Science Fiction Book 96Author 7Science Fiction0.753472
797Science Fiction Book 97Author 33Science Fiction0.752389
8134Horror Book 134Author 24Mystery0.750338
9166Science Fiction Book 166Author 13Science Fiction0.749133
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "feed", "summary": "{\n \"name\": \"feed\",\n \"rows\": 52,\n \"fields\": [\n {\n \"column\": \"book_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 62,\n \"min\": 1,\n \"max\": 190,\n \"num_unique_values\": 52,\n \"samples\": [\n 183,\n 5,\n 33\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 52,\n \"samples\": [\n \"Science Fiction Book 183\",\n \"Children Book 5\",\n \"Self-Help Book 33\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"author\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 35,\n \"samples\": [\n \"Author 17\",\n \"Author 39\",\n \"Author 53\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"Self-Help\",\n \"Fantasy\",\n \"History\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07687029380543138,\n \"min\": 0.4734882116317749,\n \"max\": 0.7992002367973328,\n \"num_unique_values\": 52,\n \"samples\": [\n 0.7440658807754517,\n 0.6536756753921509,\n 0.5650413036346436\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "code", "source": [ "def allocate_feed(ratios: dict, unseen_counts: dict, feed_size=50):\n", " alloc = {g: 0 for g in GENRES}\n", " remaining = feed_size\n", "\n", " # target counts proportional to ratios\n", " target = {g: int(round(ratios.get(g, 0.0) * feed_size)) for g in GENRES}\n", "\n", " # cap by availability\n", " for g in GENRES:\n", " alloc[g] = min(target[g], unseen_counts.get(g, 0))\n", " remaining -= alloc[g]\n", "\n", " # distribute leftovers to best ratio genres that still have items\n", " while remaining > 0:\n", " candidates = [g for g in GENRES if alloc[g] < unseen_counts.get(g, 0)]\n", " if not candidates:\n", " break\n", " g = max(candidates, key=lambda x: ratios.get(x, 0.0))\n", " alloc[g] += 1\n", " remaining -= 1\n", "\n", " return alloc\n" ], "metadata": { "id": "gErSGcl7y8Jm" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def build_mixed_feed(user_id, interactions_df, feed_size=50, random_state=42):\n", " ratios = user_interest_ratios(user_id, interactions_df)\n", "\n", " seen = set(interactions_df.loc[interactions_df.user_id == user_id, \"book_id\"].astype(int).tolist())\n", " unseen_df = books_df[~books_df.book_id.isin(seen)].copy()\n", "\n", " unseen_counts = unseen_df.groupby(\"genre\")[\"book_id\"].count().reindex(GENRES, fill_value=0).to_dict()\n", " alloc = allocate_feed(ratios, unseen_counts, feed_size=feed_size)\n", "\n", " parts = []\n", " for g, k in alloc.items():\n", " if k <= 0:\n", " continue\n", " g_df = unseen_df[unseen_df.genre == g]\n", " if len(g_df) == 0:\n", " continue\n", " parts.append(g_df.sample(n=min(k, len(g_df)), random_state=random_state))\n", "\n", " if not parts:\n", " return books_df.sample(feed_size, random_state=random_state)[[\"book_id\",\"title\",\"author\",\"genre\"]]\n", "\n", " feed = pd.concat(parts, ignore_index=True)\n", "\n", " # Shuffle / ranking\n", " feed = feed.sample(frac=1.0, random_state=random_state).reset_index(drop=True)\n", "\n", " user_vec, _ = build_user_vector(user_id, interactions_df)\n", " if user_vec is not None:\n", " idxs = [bookid_to_idx[int(b)] for b in feed[\"book_id\"].tolist()]\n", " feed_vecs = book_embeddings[idxs]\n", " feed[\"score\"] = (feed_vecs @ user_vec).astype(float)\n", " feed = feed.sort_values(\"score\", ascending=False).reset_index(drop=True)\n", "\n", " cols = [\"book_id\",\"title\",\"author\",\"genre\"]\n", " if \"score\" in feed.columns:\n", " cols.append(\"score\")\n", " return feed[cols]\n" ], "metadata": { "id": "vx2-o2a_y8La" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "uid = 1\n", "feed = build_mixed_feed(uid, interactions_df, feed_size=50)\n", "feed.head(15)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 519 }, "id": "8bTt3sVny8NQ", "outputId": "89b6018c-e140-478a-fcd1-781a6a1b9560" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " book_id title author genre score\n", "0 109 Mystery Book 109 Author 24 Mystery 0.799200\n", "1 169 Mystery Book 169 Author 58 Mystery 0.791255\n", "2 93 Mystery Book 93 Author 59 Mystery 0.789383\n", "3 37 Fantasy Book 37 Author 7 Fantasy 0.768045\n", "4 151 Science Fiction Book 151 Author 20 Science Fiction 0.758007\n", "5 79 Fantasy Book 79 Author 49 Fantasy 0.757395\n", "6 96 Science Fiction Book 96 Author 7 Science Fiction 0.753472\n", "7 97 Science Fiction Book 97 Author 33 Science Fiction 0.752389\n", "8 134 Horror Book 134 Author 24 Mystery 0.750338\n", "9 166 Science Fiction Book 166 Author 13 Science Fiction 0.749133\n", "10 91 Fantasy Book 91 Author 59 Fantasy 0.748709\n", "11 25 Science Fiction Book 25 Author 40 Science Fiction 0.748703\n", "12 146 Horror Book 146 Author 54 Mystery 0.748159\n", "13 39 Children Book 39 Author 11 Children 0.747824\n", "14 26 Science Fiction Book 26 Author 6 Science Fiction 0.747521" ], "text/html": [ "\n", "
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book_idtitleauthorgenrescore
0109Mystery Book 109Author 24Mystery0.799200
1169Mystery Book 169Author 58Mystery0.791255
293Mystery Book 93Author 59Mystery0.789383
337Fantasy Book 37Author 7Fantasy0.768045
4151Science Fiction Book 151Author 20Science Fiction0.758007
579Fantasy Book 79Author 49Fantasy0.757395
696Science Fiction Book 96Author 7Science Fiction0.753472
797Science Fiction Book 97Author 33Science Fiction0.752389
8134Horror Book 134Author 24Mystery0.750338
9166Science Fiction Book 166Author 13Science Fiction0.749133
1091Fantasy Book 91Author 59Fantasy0.748709
1125Science Fiction Book 25Author 40Science Fiction0.748703
12146Horror Book 146Author 54Mystery0.748159
1339Children Book 39Author 11Children0.747824
1426Science Fiction Book 26Author 6Science Fiction0.747521
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "feed", "summary": "{\n \"name\": \"feed\",\n \"rows\": 52,\n \"fields\": [\n {\n \"column\": \"book_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 62,\n \"min\": 1,\n \"max\": 190,\n \"num_unique_values\": 52,\n \"samples\": [\n 183,\n 5,\n 33\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 52,\n \"samples\": [\n \"Science Fiction Book 183\",\n \"Children Book 5\",\n \"Self-Help Book 33\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"author\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 35,\n \"samples\": [\n \"Author 17\",\n \"Author 39\",\n \"Author 53\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"Self-Help\",\n \"Fantasy\",\n \"History\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07687029380543138,\n \"min\": 0.4734882116317749,\n \"max\": 0.7992002367973328,\n \"num_unique_values\": 52,\n \"samples\": [\n 0.7440658807754517,\n 0.6536756753921509,\n 0.5650413036346436\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "code", "source": [ "uid = 1\n", "ratios = user_interest_ratios(uid, interactions_df)\n", "feed = build_mixed_feed(uid, interactions_df, feed_size=50)\n", "\n", "print(\"Interest ratios (top):\")\n", "print(dict(sorted(ratios.items(), key=lambda x: x[1], reverse=True)[:5]))\n", "\n", "print(\"\\nFeed genre counts:\")\n", "print(feed[\"genre\"].value_counts())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8DauKQBGy8PY", "outputId": "47d0b33d-f664-40af-ab9c-4502da8e5f97" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Interest ratios (top):\n", "{'Science Fiction': 0.2222222222222222, 'Mystery': 0.2037037037037037, 'Fantasy': 0.1111111111111111, 'Children': 0.1111111111111111, 'Poetry': 0.1111111111111111}\n", "\n", "Feed genre counts:\n", "genre\n", "Science Fiction 11\n", "Mystery 10\n", "Fantasy 6\n", "Children 6\n", "Poetry 6\n", "Romance 4\n", "History 3\n", "Self-Help 3\n", "Business 3\n", "Name: count, dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "user_feed_state = {} # user_id -> set(book_ids_shown)\n", "\n", "def get_next_feed_page(user_id, interactions_df, page_size=20):\n", " shown = user_feed_state.get(user_id, set())\n", "\n", " # add temporary \"view\" interactions for already shown books\n", " if len(shown) > 0:\n", " temp_rows = pd.DataFrame({\n", " \"user_id\": [user_id]*len(shown),\n", " \"book_id\": list(shown),\n", " \"event_type\": [\"view\"]*len(shown),\n", " \"timestamp\": [datetime.now().isoformat()]*len(shown)\n", " })\n", " temp_interactions = pd.concat([interactions_df, temp_rows], ignore_index=True)\n", " else:\n", " temp_interactions = interactions_df\n", "\n", " page = build_mixed_feed(user_id, temp_interactions, feed_size=page_size, random_state=np.random.randint(0, 10_000))\n", "\n", " # update shown state\n", " user_feed_state[user_id] = shown.union(set(page[\"book_id\"].astype(int).tolist()))\n", " return page\n", "\n", "uid = 1\n", "page1 = get_next_feed_page(uid, interactions_df, page_size=20)\n", "page2 = get_next_feed_page(uid, interactions_df, page_size=20)\n", "\n", "page1.head(10), page2.head(10)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uGK_JsAny8Sm", "outputId": "21d74f28-6d0a-4a7c-a234-0826f9f8e0bf" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "( book_id title author genre score\n", " 0 114 Mystery Book 114 Author 25 Mystery 0.809269\n", " 1 93 Mystery Book 93 Author 59 Mystery 0.789383\n", " 2 37 Fantasy Book 37 Author 7 Fantasy 0.768045\n", " 3 164 Science Fiction Book 164 Author 47 Science Fiction 0.757111\n", " 4 137 Science Fiction Book 137 Author 21 Science Fiction 0.754149\n", " 5 26 Science Fiction Book 26 Author 6 Science Fiction 0.747521\n", " 6 57 Science Fiction Book 57 Author 37 Science Fiction 0.747497\n", " 7 189 Science Fiction Book 189 Author 52 Science Fiction 0.745348\n", " 8 21 Poetry Book 21 Author 2 Poetry 0.742488\n", " 9 15 Science Fiction Book 15 Author 57 Science Fiction 0.741855,\n", " book_id title author genre score\n", " 0 124 Mystery Book 124 Author 55 Mystery 0.793147\n", " 1 14 Mystery Book 14 Author 55 Mystery 0.790424\n", " 2 151 Science Fiction Book 151 Author 20 Science Fiction 0.769735\n", " 3 96 Science Fiction Book 96 Author 7 Science Fiction 0.767569\n", " 4 97 Science Fiction Book 97 Author 33 Science Fiction 0.764578\n", " 5 166 Science Fiction Book 166 Author 13 Science Fiction 0.762528\n", " 6 25 Science Fiction Book 25 Author 40 Science Fiction 0.762403\n", " 7 59 Science Fiction Book 59 Author 32 Science Fiction 0.759857\n", " 8 91 Fantasy Book 91 Author 59 Fantasy 0.755259\n", " 9 69 Horror Book 69 Author 39 Mystery 0.747272)" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "code", "source": [ "!pip -q install gradio\n" ], "metadata": { "id": "9a12aAgIA_vC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import gradio as gr\n", "\n", "def reset_user(uid: int):\n", " user_feed_state.pop(uid, None)\n", " return f\"Reset done for user {uid}\"\n", "\n", "def show_ratios(uid: int):\n", " ratios = user_interest_ratios(uid, interactions_df)\n", " top = sorted(ratios.items(), key=lambda x: x[1], reverse=True)[:5]\n", " return \"\\n\".join([f\"{g}: {v:.2f}\" for g, v in top])\n", "\n", "def next_page(uid: int, page_size: int):\n", " page = get_next_feed_page(uid, interactions_df, page_size=page_size)\n", " return page\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"# 📚 AI Book Recommendation Feed (Mixed)\")\n", "\n", " with gr.Row():\n", " uid = gr.Number(value=1, label=\"User ID\", precision=0)\n", " page_size = gr.Slider(5, 50, value=20, step=5, label=\"Page size\")\n", "\n", " with gr.Row():\n", " btn_ratios = gr.Button(\"Show Interest Ratios\")\n", " btn_next = gr.Button(\"Load Next Page\")\n", " btn_reset = gr.Button(\"Reset Feed\")\n", "\n", " ratios_out = gr.Textbox(label=\"Top Interest Ratios\", lines=6)\n", " table_out = gr.Dataframe(label=\"Recommended Feed\")\n", "\n", " btn_ratios.click(show_ratios, inputs=[uid], outputs=[ratios_out])\n", " btn_next.click(next_page, inputs=[uid, page_size], outputs=[table_out])\n", " btn_reset.click(reset_user, inputs=[uid], outputs=[ratios_out])\n", "\n", "demo.launch(share=True)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 611 }, "id": "StJtLpJOA_xC", "outputId": "093cb4cb-8925-4e4e-c194-fc654f53bba4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", "* Running on public URL: https://508c17e883337f6f8c.gradio.live\n", "\n", "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "1-j3g8ejA_y-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "87gMTClXNYgd" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "Va5DX2V4NYdD" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "R-Je8aUuNYa1" }, "execution_count": null, "outputs": [] } ] }