Abstract
Recently, Relevance-Based Language Models have been demonstrated as an effective Collaborative Filtering approach. Nevertheless, this family of Pseudo-Relevance Feedback techniques is computationally expensive for applying them to web-scale data. Also, they require the use of smoothing methods which need to be tuned. These facts lead us to study other similar techniques with better trade-offs between effectiveness and efficiency. Specifically, in this paper, we analyse the applicability to the recommendation task of four well-known query expansion techniques with multiple probability estimates. Moreover, we analyse the effect of neighbourhood length and devise a new probability estimate that takes into account this property yielding better recommendation rankings. Finally, we find that the proposed algorithms are dramatically faster than those based on Relevance-Based Language Models, they do not have any parameter to tune (apart from the ones of the neighbourhood) and they provide a better trade-off between accuracy and diversity/novelty.
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Acknowledgments
This work was supported by the Ministerio de Economía y Competitividad of the Government of Spain under grants TIN2012-33867 and TIN2015-64282-R. The first author also wants to acknowledge the support of Ministerio de Educación, Cultura y Deporte of the Government of Spain under the grant FPU014/01724.
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Valcarce, D., Parapar, J., Barreiro, Á. (2016). Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_44
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DOI: https://doi.org/10.1007/978-3-319-30671-1_44
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