Enhancing an Interactive Recommendation System with Review-based Information Filtering

Feuerbach, J., Loepp, B., Barbu, C.-M., and Ziegler, J. (2017). Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, 1884, 2–9.


Integrating interactive faceted filtering with intelligent recommendation techniques has shown to be a promising means for increasing user control in Recommender Systems. In this paper, we extend the concept of blended recommending by automatically extracting meaningful facets from social media by means of Natural Language Processing. Concretely, we allow users to influence the recommendations by selecting facet values and weighting them based on information other users provided in their reviews. We conducted a user study with an interactive recommender implemented in the hotel domain. This evaluation shows that users are consequently able to find items fitting interests that are typically difficult to take into account when only structured content data is available. For instance, the extracted facets representing the opinions of hotel visitors make it possible to effectively search for hotels with comfortable beds or that are located in quiet surroundings without having to read the user reviews.

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Interactive Recommending: Framework, State of Research and Future Challenges

Loepp, B., Barbu, C.-M., and Ziegler, J. (2016). Proceedings of the Workshop on Engineering Computer-Human Interaction in Recommender Systems, 1705, 3–13.

Merging interactive information filtering and recommender algorithms: model and concept demonstrator

Loepp, B., Herrmanny, K., and Ziegler, J. (2015). i-com, 14(1), 5–17.

Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques

Loepp, B., Herrmanny, K., and Ziegler, J. (2015). Proceedings of the 33rd International Conference on Human Factors in Computing Systems (CHI ’15). New York, NY, USA: ACM.