Recommender System

1.      Introduction

In this information age, everyone is forced to make every decision, restaurant to go, movies to watch, or books to buy, from abundance of information. In those moments, people always turn to friends and others who are knowledgeable about our tastes and preferences for recommendations. Obtaining good recommendations becomes a vital issue when overwhelmed with decision making process.
Internet offers users accessibility to huge amounts of information, and consequently, providing good recommendations is one of the most pressing missions for the implementation of electronic environments. Therefore, the concept of Recommender Systems (RS) is introduced and studied. RS is focusing on assisting users to deal with information overload by recommending “attractive” items, based on users’ respective tastes and preferences, from huge databases or catalogues.

2.      Types of recommender system

There are three types of recommender systems, which are commonly recognized based on the adopted method of making item suggestions (Adomavicius & Tuzhilin, 2005): content-based filtering (CBF), collaborative filtering (CF), and hybrid filtering (HF).
1). CBF recommends the user items similar to the ones she preferred in the past. This approach is an efficient recommending method, only relay on the user itself, his/her preference and taste. However there are some intrinsic limitations, as it could be very difficult to extract the content for some items, and the recommending scale is very limited by the content based retrieval (same words).
2). CF recommends the user items that people (called neighbors in the literature) with similar tastes and preferences liked in the past. CF is adopted in numerous studies and researches, and is preferred by most researchers, but there are some limitations. First, a “cold start” problem, which occurs a new user is introduced, for whom there is no preference data. Moreover, the recommendation system choose neighbors only depends on the similarity between users and their neighbors, without considering global characteristics of the neighbors, which would make them less appropriate, such as their confidence or trustworthiness.
3). HF is a combination of content-based and collaborative filtering approaches..

3.      Recommender Agent

Recommender Agent (RA) is a crucial factor to a success online retailers, like Amazon.com and service providers like Netflix.com, as it provides a customized online shopping experience. It is speculated by many researchers that RAs make it possible for online merchants to guiding customers’ behavior (Gretzel & Fesenmaier, 2007). Felfernig and Gula demonstrated their view that RAs could convince the customer that some product attributes are more important than others or make the consumer more satisfied with their online shopping experience in Reference [3]. The theoretical model of RA is shown in Figure1, which shows the process of how RA stimulating consumption.

Figure1. The theoretical model

Reference

[1] Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005).

[2] U. Gretzel, D.R. Fesenmaier, Persuasion in recommender systems, International Journal of Electronic Commerce 11 (2), 2007, pp. 81–100.

[3] A. Felfernig, B. Gula, An empirical study on consumer behavior in the interaction with knowledge-based recommender applications, in: Proceedings of the 8th IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/ EEE 06).

[4] Recommendation system http://en.wikipedia.org/wiki/Recommender_system