2002 ContentBoostedCollabFilt

• (Melville et al., 2002) ⇒ Prem Melville, Raymond Mooney, Ramadass Nagarajan. (2002). “Content-Boosted Collaborative Filtering for Improved Recommendations.” In: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002).

Subject Headings: Collaborative Filtering Algorithm.

Cited BY

• Cited by ~433 http://scholar.google.com/scholar?cites=12327871543330736273&as_sdt=2000

Quotes

Abstract

• Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach. We also discuss methods to improve the performance of our hybrid system.

Introduction

• Recommender systems help overcome information overload by providing personalized suggestions based on a history of a user’s likes and dislikes. Many on-line stores provide recommending services e.g. Amazon, CDNOW, Barnes-and-Noble, IMDb, etc. There are two prevalent approaches to building recommender systems — Collaborative Filtering (CF) and Content-based (CB) recommending. CF systems work by collecting user feedback in the form of ratings for items in a given domain and exploit similarities and differences among profiles of several users in determining how to recommend an item. On the other hand, content-based methods provide recommendations by comparing representations of content contained in an item to representations of content that interests the user.
• …

Domain Description

• We demonstrate the working of our hybrid approach in the domain of movie recommendation. We use the user-movie ratings from the EachMovie1 dataset, provided by the Compaq Systems Research Center. The dataset contains rating data provided by each user for various movies. User ratings range from zero to five stars. Zero stars indicate extreme dislike for a movie and five stars indicate high praise. To have a quicker turn-around time for our experiments, we only used a subset of the EachMovie dataset. This dataset contains 7,893 randomly selected users and 1,461 movies for which content was available from the Internet Movie Database (IMDb)2. The reduced dataset has 299,997 ratings for 1,408 movies. …

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 ContentBoostedCollabFilt	Raymond J. Mooney Prem Melville Ramadass Nagarajan			Content-Boosted Collaborative Filtering for Improved Recommendations			https://www.aaai.org/Papers/AAAI/2002/AAAI02-029.pdf