More Like this

1. Recommending novel and relevant reviews to expand users’ knowledge about a product

   https://doi.org/10.1109/WIIAT50758.2020.00030  

   Ceh-Varela, Edgar; Cao, Huiping (December 2020, 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT))

   Most e-commerce websites (e.g., Amazon and TripAdvisor) show their users an initial set of useful product reviews. These reviews allow users to form a general idea about the product's characteristics. The usefulness of a review is mainly based on a score that the website users provide. Studies have shown that this score is not a good indicator of a review's actual helpfulness. Nonetheless, most past works still use it to classify a review as helpful or not. With the growing number of reviews, finding those helpful ones is a challenging task. In this work, we propose NovRev, a new unsupervised approach to recommend a personalized subset of unread useful reviews for those users looking to increase their knowledge about a product. NovRev considers an initial set of reviews as a context and recommends reviews that increase the product's information. We have extensively tested NovRev against five baseline methods, using eight real-life datasets from different product domains. The results show that NovRev can recommend novel, relevant, and diverse reviews while covering more information about the product. 

   more » « less
2. Review-Based Cross-Domain Collaborative Filtering: A Neural Framework

   Doan, T.N; Sahebi, S. (January 2019, Proceedings of the Third Workshop on Recommendation in Complex Scenarios)

   Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current cross-domain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain (DHCD) model, a cross-domain neural framework, that can simultaneously predict user ratings, and provide useful information to strengthen the suggestions and support user decision across multiple domains. Specifically, DHCD enhances the predicted ratings by jointly modeling two crucial facets of users’ product assessment: ratings and reviews. To support decisions, it models and provides natural review-like sentences across domains according to user interests and item features. This model is robust in integrating user rating and review information from more than two domains. Our extensive experiments show that DHCD can significantly outperform advanced baselines in rating predictions and review generation tasks. For rating prediction tasks, it outperforms cross-domain and single-domain collaborative filtering as well as hybrid recommender systems. Furthermore, our review generation experiments suggest an improved perplexity score and transfer of review information in DHCD. 

   more » « less
3. Multi-criteria and Review-Based Overall Rating Prediction

   https://doi.org/10.1007/978-3-030-75765-6_38  

   Ceh-Varela, Edgar; Cao, Huiping; Le, Tuan (January 2021, Pacific-Asia Conference on Knowledge Discovery and Data Mining)

   An overall rating cannot reveal the details of user’s preferences toward each feature of a product. One widespread practice of e-commerce websites is to provide ratings on predefined aspects of the product and user-generated reviews. Most recent multi-criteria works employ aspect preferences of users or user reviews to understand the opinions and behavior of users. However, these works fail to learn how users correlate these information sources when users express their opinion about an item. In this work, we present Multi-task & Multi-Criteria Review-based Rating (MMCRR), a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews. We conduct extensive experiments with three real-life datasets and six baseline models. The results show that MMCRR can reduce prediction errors while learning features better from the data. 

   more » « less
4. The Way it Makes you Feel Predicting Users’ Engagement during Interviews with Biofeedback and Supervised Learning

   https://doi.org/10.1109/RE48521.2020.00016  

   Girardi, Daniela; Ferrari, Alessio; Novielli, Nicole; Spoletini, Paola; Fucci, Davide; Huichapa, Thaide (August 2020, 2020 IEEE 28th International Requirements Engineering Conference (RE))

   null (Ed.)

   Capturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biofeedback to complement interviews with information about the engagement of the user on the discussed features and topics. We evaluate our approach by interviewing users while gathering their biometric data using an Empatica E4 wristband. Our results show that we can predict users' engagement by training supervised machine learning algorithms on the biometric data. The results of our work can be used to facilitate the prioritization of product features and to guide the interview based on users' engagement. 

   more » « less
5. Graph Bayesian Optimization for Multiplex Influence Maximization

   https://doi.org/10.1609/aaai.v38i20.30255  

   Yuan, Zirui; Shao, Minglai; Chen, Zhiqian (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Influence maximization (IM) is the problem of identifying a limited number of initial influential users within a social network to maximize the number of influenced users. However, previous research has mostly focused on individual information propagation, neglecting the simultaneous and interactive dissemination of multiple information items. In reality, when users encounter a piece of information, such as a smartphone product, they often associate it with related products in their minds, such as earphones or computers from the same brand. Additionally, information platforms frequently recommend related content to users, amplifying this cascading effect and leading to multiplex influence diffusion.This paper first formulates the Multiplex Influence Maximization (Multi-IM) problem using multiplex diffusion models with an information association mechanism. In this problem, the seed set is a combination of influential users and information. To effectively manage the combinatorial complexity, we propose Graph Bayesian Optimization for Multi-IM (GBIM). The multiplex diffusion process is thoroughly investigated using a highly effective global kernelized attention message-passing module. This module, in conjunction with Bayesian linear regression (BLR), produces a scalable surrogate model. A data acquisition module incorporating the exploration-exploitation trade-off is developed to optimize the seed set further.Extensive experiments on synthetic and real-world datasets have proven our proposed framework effective. The code is available at https://github.com/zirui-yuan/GBIM. 

   more » « less