Content-Based Filtering

A Content-Based Filtering is a filtering task that recommends items by matching content objects with user profiles.

• AKA: Cognitive Filtering, Personality-Based Filtering.
• Context:
  • It is solved by a Content-Based Recommender System.
  • …
• Counter-Example(s)
  • Collaborative Filtering.
• See: Recommender System, Filtering Task.

---

---

References

2017a

• (Recommemder-Systems.org,2017) ⇒ http://recommender-systems.org/content-based-filtering/ Retrieved on 2017-06-04, Copyright © 2012 Recommender-Systems.org All Rights Reserved.
  • Content-based filtering, also referred to as cognitive filtering, recommends items based on a comparison between the content of the items and a user profile. The content of each item is represented as a set of descriptors or terms, typically the words that occur in a document. The user profile is represented with the same terms and built up by analyzing the content of items which have been seen by the user.

    Several issues have to be considered when implementing a content-based filtering system. First, terms can either be assigned automatically or manually. When terms are assigned automatically a method has to be chosen that can extract these terms from items. Second, the terms have to be represented such that both the user profile and the items can be compared in a meaningful way. Third, a learning algorithm has to be chosen that is able to learn the user profile based on seen items and can make recommendations based on this user profile.

2017b

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Recommender_system#Overview Retrieved:2017-6-4.
  • Recommender systems typically produce a list of recommendations in one of two ways – through collaborative and content-based filtering or the personality-based approach. Collaborative filtering approaches building a model from a user's past behaviour (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. These approaches are often combined (see Hybrid Recommender Systems).

2011A

• (Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Content-Based Filtering.” In: (Sammut & Webb, 2011) p.226

2011B

• (Lops et al., 2011) ⇒ Lops, P., De Gemmis, M., & Semeraro, G. (2011). "Content-based recommender systems: State of the art and trends". In Recommender systems handbook (pp. 73-105). Springer US.
  • Abstract: Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content-based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

---