Collaborative Filtering For Similar Movie Search
Introduction
Movie recommendation framework is used to recommend movie to a user depends upon his taste. Such framework has part of suggestions what's more, is enlivened by the accomplishment of recommendation frameworks in various spaces, for example, books, TV program. Recommendation is most important break through in the television fiels. The most understood recommendation frameworks are principally in view of Collaborative Filtering (CF) and Content-based Filtering. Apart from CF many techniques are used to construct recommender system CF first step to discover the gatherings of comparative users consequently from an arrangement of users. The similitudes between users are figured utilizing correlation. Albeit Collaborative Filtering (CF) is fruitful in numerous domains, be that as it may, it has inadequacies for example, sparsity and versatility. CF utilizes user ratings to find comparative users. In any case, it is exceptionally hard to discover such since not very many movies have ratings. The effectiveness of proposed methods demonstrated using movielens data sets. Bagher et al has proposed content Movie. Temporal User Preferences can also be used for recommendation. A content-based recommendation system can be used in many domains. TF-IDF and Information Gain (IG) are used for this purpose.
Proposed Work
The proposed work uses MovieLens 100K data set [13]. This data set consists of: 100,000 ratings, ranges 0 to 5 from 943 users on 1682 movies. Each user has rated at least 20 movies. Another data file consists of informations regarding the user’s like age, gender, occupation and zip. The proposed work consists of four main stages, they are preprocessing, pivot table creation, cosine similarity generation, and recommendation section. This proposed work is developed using python and apache pig. The dataflow of this proposed system is shown in figure below.
References
1. Linden, G., Smith, B., York, J.,: Amazon.com recommendations: Item-to-item collaborative filtering. Internet Computing, 7(1), pp. 76–80. ACM (2003)
2. Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., Riedl, J.,: Movielens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the 8th international conference on intelligent user interfaces, pp. 263–266. ACM (2003)
3. Yu, Z., Zhou, X.,: Tv3p: an adaptive assistant for personalized tv. IEEE Transactions on Consumer Electronics, 50(1), pp. 393–399 (2004)
4. Smyth, B., Cotter, P.,: A personalized television listings service. Communications of ACM, 43(8), pp. 107–111 (2000)
5. Breese, J.S., Heckerman, D., Kadie, C.,: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., pp. 43–52 (1998)
6. Balabanovi´c, M.,Shoham, Y.,: Fab: content-based collaborative recommendation. Communications of the ACM, 40(3), pp. 66–72 (1997)
7. Aggarwal, Charu C.,: An introduction to recommender systems. In Recommender Systems, Springer, pp. 1–28 (2016)
8. Badrul, Sarwar, M., George Karypis, Joseph Konstan, John Reidl,: Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In: Proceedings of the 5th International Conference on Computer and Information Technology (ICCIT), (2002)
9. Bagher Rahimpour Cami, Hamid Hassanpour, Hoda Mashayekhi: A content-based movie recommender system based on temporal user preferences, 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS) pp. 121-125 (2017)
10. Bagher Rahimpour Cami, Hamid Hassanpour, Hoda Mashayekhi: User trends modeling for a content-based recommender system. Expert Systems with Applications, 87(30), pp. 209–219 (2017)
11. Balabanovi´c, M.,: An adaptive web page recommendation service. In: Proceedings of the first international conference on Autonomous agents, pp. 378–385. ACM (1997)
12. Pazzani, M., Billsus, D.,: Learning and revising user profiles: The identification of interesting web sites. Machine learning, 27(3), pp. 313–331 (1997)
13. Maxwell Harper, F., Joseph A. Konstan,: The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS), 4(19) (2015) http://grouplens.org/datasets/movielens/100k/
14. Marcel Worring, Dennis Koelma, Jan Zahálka,: Multimedia Pivot Tables for Multimedia Analytics on Image Collections. IEEE Transactions on Multimedia, 18(11), IEEE (2016)