由於網路的快速發展以及串流平台的崛起，使得人們在面對過多選擇的情況下出現困難，造成資訊超載的問題。推薦系統的重要性因此而提升。本論文提出一個基於協同過濾及標籤資訊之多媒體推薦系統，來提升推薦系統的效能。傳統的奇異分解(SVD)利用使用者的歷史紀錄而忽略了物品本身的特徵，且當資料量不足時，準確率會大幅的下降。我們提出的方法結合使用者的評分以及物品的標籤資訊，來更有效的找到使用者及物品間的潛在關係，並驗證在Last.fm和MovieLens等多媒體的資料庫中。我們的方法首先利用使用者的評分以及物品的標籤資訊來計算使用者間的相似度，並過濾掉相似度較低的使用者。接著我們提出Extend-SVD，將使用者評分和標籤喜好共同用在訓練模型上。結果顯示，我們的方法在多媒體資料庫上不只在準確度勝過傳統的協同過濾(CF)、基於內容的推薦系統(Content-based filtering)以及許多混和型過濾之推薦系統。更有效降低稀疏性問題，且在執行時間及記憶體空間有不錯的表現。

The magnitude of users and items grows rapidly due to the fast evolution of the internet and streaming service. Therefore, sparsity of the dataset becomes the major problem in recommendation system which significantly reduces the performance. To address this issue, we propose a hybrid recommendation system to incorporate collaborative filtering with contextual information. SVD is a popular collaborative filtering method since Netflix prize. It focuses on user's historical records and ignores the item's context. We combine SVD with tag information and find that it can better find out the correlation between users and items. In the proposed method, we first apply a fusion method to calculate similarity to filter out irrelevant users and items. Then, we propose extended-SVD which fits the model with both rating matrix and tag preference. With empirical evaluation on multimedia datasets including music and movie, our proposed approach reduces the sparsity level and provides better performance than SVD and other recommendation algorithms in several evaluation metrics.

[1] A. Bellogín, P. Castells, and I. Cantador, “Neighbor selection and weighting in user-based collaborative filtering: a performance prediction approach,” ACM Transactions on the Web (TWEB), vol. 8, no. 2, pp. 1–30, 2014.
[2] J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” arXiv preprint arXiv:1301.7363, 2013.
[3] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web, 2001, pp. 285–295.
[4] G. Linden, B. Smith, and J. York, “Amazon. com recommendations: Item-to-item collaborative filtering,” IEEE Internet computing, vol. 7, no. 1, pp. 76–80, 2003.
[5] J. Bennett, S. Lanning et al., “The netflix prize,” in Proceedings of KDD cup and workshop, vol. 2007. Citeseer, 2007, p. 35.
[6] M. J. Pazzani and D. Billsus, “Content-based recommendation systems,” in The adaptive web. Springer, 2007, pp. 325–341.
[7] R. Van Meteren and M. Van Someren, “Using content-based filtering for recommendation,” in Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop, vol. 30, 2000, pp. 47–56.
[8] I. Cantador, A. Bellogín, and D. Vallet, “Content-based recommendation in social tagging systems,” in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 237–240.
[9] C. Basu, H. Hirsh, W. Cohen et al., “Recommendation as classification: Using social and content-based information in recommendation,” in Aaai/iaai, 1998, pp. 714–720.
[10] M. Gorgoglione and U. Panniello, “Including context in a transactional recommender system using a pre-filtering approach: two real e-commerce applications,” in 2009 International Conference on Advanced Information Networking and Applications Workshops. IEEE, 2009, pp. 667–672.
[11] V. Codina, F. Ricci, and L. Ceccaroni, “Exploiting the semantic similarity of contextual situations for pre-filtering recommendation,” in International Conference on User Modeling, Adaptation, and Personalization. Springer, 2013, pp. 165–177.
[12] U. Panniello, A. Tuzhilin, and M. Gorgoglione, “Comparing context-aware recommender systems in terms of accuracy and diversity,” User Modeling and User-Adapted Interaction, vol. 24, no. 1, pp. 35–65, 2014.
[13] U. Panniello, A. Tuzhilin, M. Gorgoglione, C. Palmisano, and A. Pedone, “Experimental comparison of pre-vs. post-filtering approaches in context-aware recommender systems,” in Proceedings of the third ACM conference on Recommender systems, 2009, pp. 265–268.
[14] X. Ramirez-Garcia and M. García-Valdez, “Post-filtering for a restaurant context-aware recommender system,” in Recent advances on hybrid approaches for designing intelligent systems. Springer, 2014, pp. 695–707.
[15] M. A. Chatti, S. Dakova, H. Thüs, and U. Schroeder, “Tag-based collaborative filtering recommendation in personal learning environments,” IEEE Transactions on learning technologies, vol. 6, no. 4, pp. 337–349, 2013.
[16] M. Balabanović and Y. Shoham, “Fab: content-based, collaborative recommendation,” Communications of the ACM, vol. 40, no. 3, pp. 66–72, 1997.
[17] T. Hornung, C.-N. Ziegler, S. Franz, M. Przyjaciel-Zablocki, A. Schätzle, and G. Lausen, “Evaluating hybrid music recommender systems,” in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 1. IEEE, 2013, pp. 57–64.
[18] K. H. Tso-Sutter, L. B. Marinho, and L. Schmidt-Thieme, “Tag-aware recommender systems by fusion of collaborative filtering algorithms,” in Proceedings of the 2008 ACM symposium on Applied computing, 2008, pp. 1995–1999.
[19] M. Zhang, X. Yan, and H. Peng, “Recommendation algorithm based on user attributes and tag preferences,” in Journal of Physics: Conference Series, vol. 1684, no. 1. IOP Publishing, 2020, p. 012081.
[20] M. Riyahi and M. K. Sohrabi, “Providing effective recommendations in discussion groups using a new hybrid recommender system based on implicit ratings and semantic similarity,” Electronic Commerce Research and Applications, vol. 40, p. 100938, 2020.
[21] Y. Zheng, Y. Wang, L. Zhang, J. Wang, and Q. Qi, “A tag-based integrated diffusion model for personalized location recommendation,” in International Conference on Neural Information Processing. Springer, 2017, pp. 327–337.
[22] J. Zhang, Y. Yang, Q. Tian, L. Zhuo, and X. Liu, “Personalized social image recommendation method based on user-image-tag model,” IEEE Transactions on Multimedia, vol. 19, no. 11, pp. 2439–2449, 2017.
[23] A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver, “Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering,” in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 79–86.
[24] A. Nanopoulos, D. Rafailidis, P. Symeonidis, and Y. Manolopoulos, “Musicbox: Personalized music recommendation based on cubic analysis of social tags,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 2, pp. 407– 412, 2009.
[25] P. Symeonidis, A. Nanopoulos, and Y. Manolopoulos, “A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 2, pp. 179–192, 2009.
[26] O. N. Osmanli and İ. H. Toroslu, “Using tag similarity in svd-based recommendation systems,” in 2011 5th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2011, pp. 1–4.
[27] R. Krestel, P. Fankhauser, and W. Nejdl, “Latent dirichlet allocation for tag recommendation,” in Proceedings of the third ACM conference on Recommender systems, 2009, pp. 61–68.
[28] N. Hariri, B. Mobasher, and R. Burke, “Context-aware music recommendation based on latenttopic sequential patterns,” in Proceedings of the sixth ACM conference on Recommender systems, 2012, pp. 131–138.
[29] A. S. R. W. W. Umbrath and L. Hennig, “A hybrid plsa approach for warmer cold start in folksonomy recommendation,” Recommender Systems & the Social Web, pp. 10–13, 2009.
[30] B. Shao, D. Wang, T. Li, and M. Ogihara, “Music recommendation based on acoustic features and user access patterns,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 8, pp. 1602–1611, 2009.
[31] M. Soleymani, A. Aljanaki, F. Wiering, and R. C. Veltkamp, “Content-based music recommendation using underlying music preference structure,” in 2015 IEEE International Conference on Multimedia and Expo (ICME), 2015, pp. 1–6.
[32] Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in 2008 Eighth IEEE International Conference on Data Mining. Ieee, 2008, pp. 263–272.
[33] M. Pacula, “A matrix factorization algorithm for music recommendation using implicit user feedback,” 2009.
[34] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Application of dimensionality reduction in recommender system-a case study,” Minnesota Univ Minneapolis Dept of Computer Science, Tech. Rep., 2000.
[35] F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” Acm transactions on interactive intelligent systems (tiis), vol. 5, no. 4, pp. 1–19, 2015.
[36] T. Bertin-Mahieux, D. P. Ellis, B. Whitman, and P. Lamere, “The million song dataset,” in Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011), 2011.