Greedy recommending is not always optimal

Abstract. Recommender systems help users to find objects or documents on web sites. In many cases it is not easy to know in advance by whom and for what purpose a web site will be used. This makes it difficult for many applications to define adequate recommendations in advance. Therefore recommendations are typically generated dynamically. Recommendations are based on analysis of user data (social filtering) or content (content-based) or a mixture of these. Current methods optimise the quality of recommended objects (e.g. the probability that it is the target of the user, or the estimated interestingness). Even with recommendations, many steps are often needed to locate the desired information. This changes the task to what we call “sequential recommending”: a series of recommendations in which the user indicates his preference, leading to a target object. Here we argue that in sequential recommending a series of normal, “greedy”, recommendings is not always the strategy that minimises the number of steps in the search. Greedy sequential recommending conflicts with the need to explore the entire space and may lead to recommending series that require more steps (mouse clicks) from the user than necessary. We illustrate this with an example, analyse when this is so and outline a more efficient recommendation method.

In Proc. of the 1st European Web Mining Forum (EWMF), 2003 Greedy recommending is not always optimal

Abstract. Recommender systems help users to £nd objects or documents on web sites. In many cases it is not easy to know in advance by whom and for what purpose a web site will be used. This makes it dif£cult for many applications to de£ne adequate recommendations in advance. Therefore recommendations are typically generated dynamically. Recommendations are based on analysis of user data (social £ltering) or content (content-based) or a mixture of these. Current methods optimise the quality of recommended objects (e.g. the probability that it is the target of the user, or the estimated interestingness). Even with recommendations, many steps are often needed to locate the desired information. This changes the task to what we call “sequential recommending”: a series of recommendations in which the user indicates his preference, leading to a target object. Here we argue that in sequential recommending a series of normal, “greedy”, recommendings is not always the strategy that minimises the number of steps in the search. Greedy sequential recommending con¤icts with the need to explore the entire space and may lead to recommending series that require more steps (mouse clicks) from the user than necessary. We illustrate this with an example, analyse when this is so and outline a more ef£cient recommendation method.

In Post-Proc of the 1st European Web Mining Forum, LNAI, 2004. Greedy recommending is not always optimal

Abstract. Recommender systems suggest objects to users. One form recommends documents or other objects to users searching information on a web site. A recommender system can use data about a user to recommend information, for example web pages. Current methods for recommending are aimed at optimising single recommendations. However, usually a series of interactions is needed to find the desired information. Here we argue that in interactive recommending a series of normal, ‘greedy’, recommendings is not the strategy that minimises the number of steps in the search. Greedy sequential recommending conflicts with the need to explore the entire space of user preferences and may lead to recommending series that require more steps (mouse clicks) from the user than necessary. We illustrate this with an example, analyse when this is so and outline when greedy recommending is not the most efficient.