Course:LIBR557/2020WT2/content-based filtering

Content-Based Filtering

Content-Based Filtering is a common approach when designing recommender systems. It is based on a description of the item and a profile of the user’s preferences. Content-based Filtering recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features.

User Profile

a user profile is built to indicate the type of item this user likes. The algorithm tries to recommend items that are similar to those that a user liked in the past or is examining in the present. It does not rely on a user sign-in mechanism to generate this often-temporary profile. Various candidate items are compared with items previously rated by the user and the best-matching items are recommended.

Algorithm

To abstract the features of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf–idf representation. The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques. Simple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques such as Bayesian Classifiers, cluster analysis, decision trees, and artificial neural networks in order to estimate the probability that the user is going to like the item.

Challenge

Whether the system is able to learn user preferences from users' actions regarding one content source and use them across other content types. When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended. For example, recommending news articles based on browsing of news is useful, but would be much more useful when music, videos, products, discussions etc. from different services can be recommended based on news browsing. To overcome this, most content-based recommender systems now use some form of hybrid system.

Relationship

Content-Based Filtering systems can also include opinion-based recommender systems. In some cases, users can leave text review or feedback on the items. These user-generated texts are implicit data for the recommender system because they are potentially rich resource of both feature/aspects of the item, and users' evaluation/sentiment to the item.

References

Aggarwal, Charu C. (2016). Recommender Systems: The Textbook. Springer. ISBN 9783319296579.

Peter Brusilovsky (2007). The Adaptive Web. p. 325. ISBN 978-3-540-72078-2.

D.H. Wang, Y.C. Liang, D.Xu, X.Y. Feng, R.C. Guan(2018), "A content-based recommender system for computer science publications", Knowledge-Based Systems, 157: 1-9

Blanda, Stephanie (May 25, 2015). "Online Recommender Systems – How Does a Website Know What I Want?". American Mathematical Society. Retrieved October 31, 2016.

X.Y. Feng, H. Zhang, Y.J. Ren, P.H. Shang, Y. Zhu, Y.C. Liang, R.C. Guan, D. Xu, (2019), "The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study", Journal of Medical Internet Research, 21 (5): e12957