Collaborative Filtering (CF)-based Recommendation Algorithm

A Collaborative Filtering (CF)-based Recommendation Algorithm is an supervised numeric prediction algorithm that can be implemented by a collaborative filtering system to solve a collaborative filtering task (which provides preference data).

• Context:
  • It can range from being an Item-based Collaborative Filtering Algorithm to being a User-based Collaborative Filtering Algorithm.
  • It can range from being a Model-based Collaborative Filtering Algorithm to being a Model-free Collaborative Filtering Algorithm.
  • …
• Example(s):
  • a Model-based CF Recommendation Algorithm, such as:
    • a Latent Factor Recommender Algorithm (such as a matrix factorization-based recommendation algorithm, which assumes that there exits a set of factors that influence the item ratings).
    • a Deep Learning-based Recommendation Algorithm.
  • a Model-Free CF Algorithm.
  • …
• Counter-Example(s):
  • Content-based Item Recommendation Algorithm.
  • Latent Dirichlet Allocation.
• See: Social Network Analysis, Cold-Start Problem, Hybrid Item(s) Recommendation Algorithm.

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References

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/collaborative_filtering Retrieved:2017-5-11.
  • Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.

    In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person. For example, a collaborative filtering recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes). Note that these predictions are specific to the user, but use information gleaned from many users. This differs from the simpler approach of giving an average (non-specific) score for each item of interest, for example based on its number of votes.

    In the more general sense, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data, such as financial service institutions that integrate many financial sources; or in electronic commerce and web applications where the focus is on user data, etc. The remainder of this discussion focuses on collaborative filtering for user data, although some of the methods and approaches may apply to the other major applications as well.

2015

• (Wang et al., 2015) ⇒ Hao Wang, Naiyan Wang, and Dit-Yan Yeung. (2015). “Collaborative Deep Learning for Recommender Systems.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783273
  • QUOTE: Collaborative filtering (CF) algorithm|Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recently advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix.

2013

• https://en.wikipedia.org/wiki/File:Collaborative_filtering.gif
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2011

• (Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Collaborative Filtering.” In: (Sammut & Webb, 2011) p.189
  • QUOTE: Collaborative Filtering (CF) refers to a class of techniques used in recommender systems, that recommend items to users that other users with similar tastes have liked in the past. CF methods are commonly sub-divided into neighborhood-based and model-based approaches. In neighborhood-based approaches, a subset of users are chosen based on their similarity to the active user, and a weighted combination of their ratings is used to produce predictions for this user. In contrast, model-based approaches assume an underlying structure to users' rating behavior, and induce predictive models based on the past ratings of all users.

2009

• (Konstas et al., 2009) ⇒ Ioannis Konstas, Vassilios Stathopoulos, and Joemon M. Jose. (2009). “On Social Networks and Collaborative Recommendation.” In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ISBN:978-1-60558-483-6 doi:10.1145/1571941.1571977
  • QUOTE: … We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.

1999

• (Herlocker et al., 1999) ⇒ Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl. (1999). “An Algorithmic Framework for Performing Collaborative Filtering.” In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. doi:10.1145/312624.312682
  • QUOTE: Automated collaborative filtering is quickly becoming a popular technique for reducing information overload, often as a technique to complement content-based information filtering systems. In this paper we present an algorithmic framework for performing collaborative filtering and new algorithmic elements that increase the accuracy of collaborative prediction algorithms. We then present a set of recommendations on selection of the right collaborative filtering algorithmic components.

1998

• (Breese et al., 1998) ⇒ John S. Breese, David Heckerman, and Carl Kadie. (1998). “Empirical Analysis of Predictive Algorithms for Collaborative Filtering." In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998).

1992

• (Goldberg et al., 1992) ⇒ David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. (1992). “Using Collaborative Filtering to Weave An Information Tapestry.” In: Communications of the ACM Journal, 35(12). doi:10.1145/138859.138867
  • QUOTE: The Tapestry experimental mail system developed at the Xerox Palo Alto Research Center is predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering by recording their reactions to documents they read.

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