More Like this

1. Context-aware Multi-stakeholder Recommender Systems

   https://doi.org/10.32473/flairs.v35i.130573  

   Arabghalizi, Tahereh ; Labrinidis, Alexandros ( May 2022 , The International FLAIRS Conference Proceedings)

   Traditional recommender systems help users find the most relevant products or services to match their needs and preferences. However, they overlook the preferences of other sides of the market (aka stakeholders) involved in the system. In this paper, we propose to use contextual bandit algorithms in multi-stakeholder platforms where a multi-sided relevance function with adjusting weights is modeled to consider the preferences of all involved stakeholders. This algorithm sequentially recommends the items based on the contextual features of users along with the priority of the stakeholders and their relevance to the items.Our extensive experimental results on a dataset consisting of MovieLens (1m), IMDB (81k+), and a synthetic dataset show that our proposed approach outperforms the baseline methods and provides a good trade-off between the satisfaction of different stakeholders over time. 

   more » « less
2. A Ranked Bandit Approach for Multi-stakeholder Recommender Systems

   Arabghalizi, Tahereh ; Labrinidis, Alexandros ( September 2022 , Workshop of Multi-Objective Recommender Systems (MORS’22))

   Recommender systems traditionally find the most relevant products or services for users tailored to their needs or interests but they ignore the interests of the other sides of the market (aka stakeholders). In this paper, we propose to use a Ranked Bandit approach for an online multi-stakeholder recommender system that sequentially selects top 𝑘 items according to the relevance and priority of all the involved stakeholders. We presented three different criteria to consider the priority of each stakeholder when evaluating our approach. Our extensive experimental results on a movie dataset showed that the contextual multi-armed bandits with a relevance function make a higher level of satisfaction for all involved stakeholders in the long term. Keywords: Multi-stakeholder Recommender Systems; Multi-armed Bandits; Ranked Bandit; 

   more » « less
3. Fairness and Control of Exposure in Two-sided Markets

   https://doi.org/10.1145/3471158.3472226  

   Joachims, Thorsten ( January 2021 , ACM SIGIR International Conference on Theory of Information Retrieval)

   A large number of two-sided markets are now mediated by search and recommender systems, ranging from online retail and streaming entertainment to employment and romantic-partner matching. I will discuss in this talk how the design decisions that go into these search and recommender systems carry substantial power in shaping markets and allocating opportunity to the participants. This does not only raise legal and fairness questions, but also questions about how these systems shape incentives and the long-term effectiveness of the market. At the core of these questions lies the problem of where to rank each item, and how this affects both sides of the market. While it is well understood how to maximize the utility to the users, this talk focuses on how rankings affect the items that are being ranked. From the items perspective, the ranking system is an arbiter of exposure and thus economic opportunity. I will discuss how machine learning algorithms that follow the conventional Probability Ranking Principle [1] can lead to undesirable and unfair exposure allocation for both exogenous and endogenous reasons. Exogenous reasons often manifest themselves as biases in the training data, which then get reflected in the learned ranking policy. But even when trained with unbiased data, reasons endogenous to the system can lead to unfair or undesirable allocation of opportunity. To overcome these challenges, I will present new machine learning algorithms [2,3,4] that directly address both endogenous and exogenous factors, allowing the designer to tailor the ranking policy to be appropriate for the specific two-sided market. 

   more » « less
4. Customers’ Behavioural Immune System Responses to the COVID-19 Pandemic: A conceptual framework

   https://doi.org/10.54055/ejtr.v30i.2264  

   Huang, Arthur ; Farboudi Jahromi, Melissa ; Marquez, Julia ( March 2022 , European Journal of Tourism Research)

   The COVID-19 pandemic has imposed various obstacles and restrictions for the tourism and hospitality industry. This paper adopts the concept of the behavioural immune system to discuss tourism and hospitality customers’ potential behaviours during the pandemic and provide business strategies that can address these behaviours. The behavioural immune system is a motivational system that determines individuals’ behaviours to pathogen infection. First, this study introduces the mechanism of the behavioural immune system including environmental evaluation and aversive perception, aversive emotional and cognitive responses, and avoidance behaviours. It also provides examples in the guest service context to better portray the mechanism. Second, the study suggests specific measures for tourism and hospitality businesses that may help them to prevent the aversive and avoidance responses of customers triggered by their behavioural immune system during the pandemic. Then, the study integrates customers’ responses and businesses’ measures in a framework, which extends the literature on customers’ behaviour in the hospitality and tourism context. To the authors’ knowledge, this is the first time the concept of behavioural immune system is adopted to discuss customers’ behaviours towards tourism and hospitality services during a pandemic. 

   more » « less
5. 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. 

   more » « less