Authors:
María del Carmen Rodríguez-Hernández
1
;
Sergio Ilarri
1
;
Raquel Trillo-Lado
1
and
Francesco Guerra
2
Affiliations:
1
University of Zaragoza, Spain
;
2
University of Modena and Reggio Emilia, Italy
Keyword(s):
Keyword-based Search, Recommendation Systems, Mobile Computing, Hidden Markov Model, Information Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Non-Relational Databases
;
Performance Evaluation and Benchmarking
;
Query Languages and Query Processing
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
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 Informati
on Retrieval technique in our preliminary experimental setup.
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