<jupyter_start><jupyter_text>Find user Recommendation<jupyter_code>user_id = 134
recommeded = model.recommend(user_id, sparse_user_item)
restaurant = []
scores = []
for item in recommeded:
    idx,score = item
    restaurant.append(dataset.name.loc[dataset.placeID==idx].iloc[0])
    scores.append(score)
print(pd.DataFrame({"restaurant":restaurant, "scores":scores}))<jupyter_output>restaurant    scores
0              la parroquia  0.899414
1  Preambulo Wifi Zone Cafe  0.879581
2      Mariscos El Pescador  0.675921
3                 emilianos  0.627210
4      Restaurant Orizatlan  0.558932
5                <NAME>i  0.555523
6              Cafe Chaires  0.533406
7       <NAME>  0.532920
8                 shi ro ie  0.518662
9                la Cantina  0.516255
{'fraction_non_alphanumeric': 0.08723958333333333, 'fraction_numerical': 0.109375, 'mean_word_length': 2.560185185185185, 'pattern_counts': {'":': 2, '<': 6, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '664775', 'license': 'Apache-2.0', 'n_tokens_mistral': 328, 'n_tokens_neox': 272, 'n_words': 61, 'path': 'ALS implementation/Restaurant recommendation .ipynb', 'repo_name': 'InternityFoundation/Machine-Learning-Shaurya', 'size': '784'}