Prof. Krisztian Balog

@conference{Balog2020,

title = {Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations},

author = {Krisztian Balog and Filip Radlinski},

url = {https://dl.acm.org/doi/pdf/10.1145/3397271.3401032},

doi = {https://doi.org/10.1145/3397271.3401032},

year  = {2020},

date = {2020-07-01},

booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20)},

pages = {329–338},

address = {New York},

abstract = {Explanations have a large effect on how people respond to recommendations. However, there are many possible intentions a system may have in generating explanations for a given recommendation -from increasing transparency, to enabling a faster decision, to persuading the recipient. As a good explanation for one goal may not be good for others, we address the questions of (1) how to robustly measure if an explanation meets a given goal and (2) how the different goals interact with each other. Specifically, this paper presents a first proposal of how to measure the quality of explanations along seven common goal dimensions catalogued in the literature. We find that the seven goals are not independent, but rather exhibit strong structure. Proposing two novel explanation evaluation designs, we identify challenges in evaluation, and provide more efficient measurement approaches of explanation quality.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Balog2019,

title = {Transparent, Scrutable and Explainable User Models for Personalized Recommendation},

author = {Krisztian Balog and Filip Radlinski and Shushan Arakelyan},

url = {https://dl.acm.org/doi/pdf/10.1145/3331184.3331211},

doi = {https://doi.org/10.1145/3331184.3331211},

year  = {2019},

date = {2019-07-25},

booktitle = {Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19)},

pages = {265–274},

abstract = {Most recommender systems base their recommendations on implicit or explicit item-level feedback provided by users. These item ratings are combined into a complex user model, which then predicts the suitability of other items. While effective, such methods have limited scrutability and transparency. For instance, if a user's interests change, then many item ratings would usually need to be modified to significantly shift the user's recommendations. Similarly, explaining how the system characterizes the user is impossible, short of presenting the entire list of known item ratings. In this paper, we present a new set-based recommendation technique that permits the user model to be explicitly presented to users in natural language, empowering users to understand recommendations made and improve the recommendations dynamically. While performing comparably to traditional collaborative filtering techniques in a standard static setting, our approach allows users to efficiently improve recommendations. Further, it makes it easier for the model to be validated and adjusted, building user trust and understanding.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Zhang2018,

title = {Ad Hoc Table Retrieval using Semantic Similarity},

author = {Shuo Zhang and Krisztian Balog},

url = {https://arxiv.org/pdf/1802.06159.pdf},

doi = {10.1145/3178876.3186067},

year  = {2018},

date = {2018-02-16},

booktitle = {Proceedings of The Web Conference 2018 (WWW’18)},

pages = {1553-1562},

abstract = {We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}