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1. 2SRS: A Two-Sided Recommender System to Connect Local Businesses to Bus Passengers

   https://doi.org/10.1109/MDM52706.2021.00028  

   Arabghalizi, Tahereh ; Labrinidis, Alexandros ( June 2021 , 22nd IEEE International Conference on Mobile Data Management (MDM 2021))

   null (Ed.)

   Recommender systems are widely used to help customers find the most relevant and personalized products or services tailored to their preferences. However, traditional systems ignore the preferences of the other side of the market, e.g., “product suppliers” or “service providers”, towards their customers. In this paper, we present 2SRS a Two-Sided Recommender System that recommends coupons, supplied by local businesses, to passerby while considering the preferences of both sides towards each other. For example, some passerby may only be interested in coffee shops whereas certain businesses may only be interested in sending coupons to new customers only. Our experimental results show that 2SRS delivers higher satisfaction when considering both sides of the market compared to the baseline methods. 

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2. A Weighted Statistical Network Modeling Approach to Product Competition Analysis

   https://doi.org/10.1155/2022/9417869  

   Cui, Yaxin ; Ahmed, Faez ; Sha, Zhenghui ; Wang, Lijun ; Fu, Yan ; Contractor, Noshir ; Chen, Wei ( January 2022 , Complexity)

   Suweis, Samir (Ed.)

   Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not), while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences (i.e., aggregated choice decisions) and car competition (i.e., customers’ co-consideration decisions) together using weighted directed networks. 

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3. Omnichannel Assortment Optimization Under the Multinomial Logit Model with a Features Tree

   https://doi.org/10.1287/msom.2021.1001  

   Lo, Venus ; Topaloglu, Huseyin ( March 2022 , Manufacturing & Service Operations Management)

   Problem definition: We consider the assortment optimization problem of a retailer that operates a physical store and an online store. The products that can be offered are described by their features. Customers purchase among the products that are offered in their preferred store. However, customers who purchase from the online store can first test out products offered in the physical store. These customers revise their preferences for online products based on the features that are shared with the in-store products. The full assortment is offered online, and the goal is to select an assortment for the physical store to maximize the retailer’s total expected revenue. Academic/practical relevance: The physical store’s assortment affects preferences for online products. Unlike traditional assortment optimization, the physical store’s assortment influences revenue from both stores. Methodology: We introduce a features tree to organize products by features. The nonleaf vertices on the tree correspond to features, and the leaf vertices correspond to products. The ancestors of a leaf correspond to features of the product. Customers choose among the products within their store’s assortment according to the multinomial logit model. We consider two settings; either all customers purchase online after viewing products in the physical store, or we have a mix of customers purchasing from each store. Results: When all customers purchase online, we give an efficient algorithm to find the optimal assortment to display in the physical store. With a mix of customers, the problem becomes NP-hard, and we give a fully polynomial-time approximation scheme. We numerically demonstrate that we can closely approximate the case where products have arbitrary combinations of features without a tree structure and that our fully polynomial-time approximation scheme performs remarkably well. Managerial implications: We characterize conditions under which it is optimal to display expensive products with underrated features and expose inexpensive products with overrated features. 

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4. Design and Evaluation of Hybrid Search for American Sign Language to English Dictionaries: Making the Most of Imperfect Sign Recognition

   https://doi.org/10.1145/3491102.3501986  

   Hassan, Saad ; Amin, Akhter Al ; Gordon, Alexis ; Lee, Sooyeon ; Huenerfauth, Matt ( April 2022 , Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems)

   Searching for the meaning of an unfamiliar sign-language word in a dictionary is difficult for learners, but emerging sign-recognition technology will soon enable users to search by submitting a video of themselves performing the word they recall. However, sign-recognition technology is imperfect, and users may need to search through a long list of possible results when seeking a desired result. To speed this search, we present a hybrid-search approach, in which users begin with a video-based query and then filter the search results by linguistic properties, e.g., handshape. We interviewed 32 ASL learners about their preferences for the content and appearance of the search-results page and filtering criteria. A between-subjects experiment with 20 ASL learners revealed that our hybrid search system outperformed a video-based search system along multiple satisfaction and performance metrics. Our findings provide guidance for designers of video-based sign-language dictionary search systems, with implications for other search scenarios. 

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5. Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search

   https://doi.org/10.1145/3209978.3209983  

   Ahmad, Wasi Uddin ; Chang, Kai-Wei ; Wang, Hongning ( July 2018 , SIGIR '18 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval)

   Modern web search engines exploit users' search history to personalize search results, with a goal of improving their service utility on a per-user basis. But it is this very dimension that leads to the risk of privacy infringement and raises serious public concerns. In this work, we propose a client-centered intent-aware query obfuscation solution for protecting user privacy in a personalized web search scenario. In our solution, each user query is submitted with l additional cover queries and corresponding clicks, which act as decoys to mask users' genuine search intent from a search engine. The cover queries are sequentially sampled from a set of hierarchically organized language models to ensure the coherency of fake search intents in a cover search task. Our approach emphasizes the plausibility of generated cover queries, not only to the current genuine query but also to previous queries in the same task, to increase the complexity for a search engine to identify a user's true intent. We also develop two new metrics from an information theoretic perspective to evaluate the effectiveness of provided privacy protection. Comprehensive experiment comparisons with state-of-the-art query obfuscation techniques are performed on the public AOL search log, and the propitious results substantiate the effectiveness of our solution. 

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