Content-boosted Matrix Factorization Techniques for Recommender Systems
نویسندگان
چکیده
Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable.
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ورودعنوان ژورنال:
- Statistical Analysis and Data Mining
دوره 6 شماره
صفحات -
تاریخ انتشار 2013