Towards Keyword-based Pull Recommendation Systems

Due to the high availability of data, users are frequently overloaded with a huge amount of alternatives when they need to choose a particular item. This has motivated an increased interest in research on recommendation systems, which filter the options and provide users with suggestions about specific elements (e.g., movies, restaurants, hotels, books, etc.) that are estimated to be potentially relevant for the user. In this paper, we describe and evaluate two possible solutions to the problem of identification of the type of item (e.g., music, movie, book, etc.) that the user specifies in a pull-based recommendation (i.e., recommendation about certain types of items that are explicitly requested by the user). We evaluate two alternative solutions: one based on the use of the Hidden Markov Model and another one exploiting Information Retrieval techniques. Comparing both proposals experimentally, we can observe that the Hidden Markov Model performs generally better than the Informatio n Retrieval technique in our preliminary experimental setup.