Abstract
The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.
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Notes
- 1.
Recall that the dot product between two vectors \(x,y \in \mathbb{R}^{f}\) is defined as: \(x^{T}y =\sum _{ k=1}^{f}x_{k} \cdot y_{k}\).
- 2.
Notational clarification: With other neighborhood models it was beneficial to use Sk(i; u), which denotes the k items most similar to i among those rated by u. Hence, if u rated at least k items, we will always have | Sk(i; u) | = k, regardless of how similar those items are to i. However, | Rk(i; u) | is typically smaller than k, as some of those items most similar to i were not rated by u.
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Koren, Y., Bell, R. (2015). Advances in Collaborative Filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_3
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