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A personalized product recommendation model in e-commerce based on retrieval strategyNguyen · 2024In recent years, online shopping is one of the routine parts in people’s life. It is convenient and takes less effort to purchase it. Regarding the increasing revolution of e-commerce businesses, recommendation engine plays a crucial role in them. Recommendation engines are very popular and easy to implement to their platform nowadays. Due to the extremely high competition of e-commerce businesses, the operation needs to integrate the recommender wisely. This study presents a comprehensive approach to improving user experience and engagement on e-commerce platforms through the implementation of an implicit personalized product recom­ mendation engine. Collaborating with the H&M Group, the research combines the strength of each recom­ mending algorithms which are collaborative filtering, popularity, and Bayesian personalized ranking to develop a robust recommendation system. By leveraging a retrieval strategy that combines multiple algorithmic tech­ niques and evaluating candidates using machine learning models which comprise LightGBM and Deep Neural Network, the study achieves promising results. The authors utilize two popular technical metrics to evaluate their models which are mean average precision at K candidates (MAP@K) and mean average recall at K candidates (MAR@K). The empirical result indicates that the LightGBM model has remarkable performance than Deep Neural Network model, which are 0.06 versus 0.02 respectively in MAP@K and 0.03 versus 0.01 respectively in MAR@K when both recommending ways is at 50 items. Overall, this research contributes a novel framework that addresses the challenges of analyzing large-scale data, cold-start problems, and personalization, thereby enhancing the user experience, and driving sales on e-commerce platforms.

UX of AI & PersonalizationResearch Papers

Service-Aware Personalized Item RecommendationReceived February · 2022Current recommender systems employ item-centric properties to estimate ratings and present the results to the user. However, recent studies highlight the fact that the stages of item fruition also involve extrinsic factors, such as the interaction with the service provider before, during and after item selection. In other words, a holistic view of consumer experience, including local properties of items, as well as consumers’ perceptions of item fruition, should be adopted to enhance user awareness and decision-making. In this work, we integrate recommender systems with service models to reason about the different stages of item fruition. By exploiting the Service Journey Maps to define service-based item and user profiles, we develop a novel family of recommender systems that evaluate items by taking preference management and overall consumer experience into account. Moreover, we introduce a two-level visual model to provide users with different information about recommendation results: (i) the higher level summarizes consumer experience about items and supports the identification of promising suggestions within a possibly long list of results; (ii) the lower level enables the exploration of detailed data about the local properties of items. In a user test instantiated in the home-booking domain, we compared our models to standard recommender systems. We found that the service-based algorithms that only use item fruition experience excel in ranking and minimize the error in rating estimation. Moreover, the combination of data about item fruition experience and item properties achieves slightly lower recommendation performance; however, it enhances users’ perceptions of the awareness and the decision-making support provided by the system. These results encourage the adoption of service-based models to summarize user preferences and experience in recommender systems. INDEX TERMS Information filtering, recommender systems, data visualization, service modeling.

UX of AI & PersonalizationResearch Papers

Interactive Music Genre Exploration with Visualization and Mood ControlYu Liang · 2021Recommender systems can be used to help users discover novel items and explore new tastes, for example in music genre explo- ration. However, little work has studied how to improve users’ understandability and acceptance of the novel items as well as sup- port users to explore a new domain. In this paper, we investigate how two different visualizations and mood control influence the perceived control, informativeness and understandability of a mu- sic genre exploration tool, and further to improve the helpfulness for new music genre exploration. Specifically, we compare a bar chart visualization used by earlier work to a contour plot which allows users to compare their musical preferences with both the recommended tracks as well as the new genre. Mood control is implemented with two sliders to set a preferred mood on energy and valence features (that correlate with psychological mood di- mensions). In the online user study, mood control was manipulated between subjects, and the visualizations were compared within subjects. During the study (N=102), we measured users’ subjective perceptions, experiences and the interactions with the system. Our results show that the contour plot visualization is perceived more helpful to explore new genres than the bar chart visualization, as the contour plot is perceived to be more informative and under- standable. Users spent significantly more time and used the mood control more in the contour plot than in the bar chart visualiza- tion. Overall, our results show that the contour plot visualization combined with mood control serves as the most helpful way for new music genre exploration, because the mood control is easier to understand and use when made transparent via an informative visualization. CCS CONCEPTS • Human-centered computing →User studies; Information visualization; User interface design; • Information systems → Recommender systems; Personalization. IUI ’21, April 14–17, 2021, College Station, TX, USA © 2021 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-8017-1/21/04. https://doi.org/10.1145/3397481.3450700

UX of AI & PersonalizationResearch Papers

A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendationsPipat Thontirawong b, Punjaporn Chinchanachokchai c · 2021Recommender systems are used in e-Commerce websites to make product recommendations or deliver person­ alized content to users. We constructed a beer recommendation program using review data from existing online community to test the hypotheses. This research aims to bridge the gap between marketing and computer science by investigating the moderating effects of consumer knowledge (expertise) on the performance and evaluation of two widely-used recommendation systems – user-based collaborative filtering and content-based. The results show that expert consumers prefer user-based collaborative filtering systems, whereas there is no difference between the two systems among novice consumers. Theoretical and managerial implications are discussed.

UX of AI & PersonalizationResearch Papers

An Effective Clustering‑Based Web Page Recommendation Framework for E‑Commerce WebsitesHarpreet Singh · 2020The burgeoning e-commerce market has presented companies with the opportunity to grow their businesses through online platforms. But, the researchers have concluded that just 2.86% of e-commerce website visits lead to a purchase and one of the reasons for this missed opportunity is an unpleasant website browsing experience. Therefore, a pleasant browsing experience is the need of the hour whereby the web page recommendation systems (WPRS) provide high-quality navigation experience by providing suggestions about the web pages of interest and by taking the website users to their desired web pages in fewer clicks. In this context, this paper presents a method to improve the browsing experience of the website users by propos- ing two hybrid algorithms based on clustering for web page recommendation systems, namely a hybrid partitioning-based heuristic sequence clustering (HSC) algorithm inspired from K-medoid and DBSCAN algorithms and a hybrid tree-based sequence clustering (TSC) algorithm inspired from B-Trees and BIRCH algorithm. The testing has been performed using CTI, BMSWebView1, BMSWebView2 and MSNBC datasets. To measure the performance, the algorithm considered for the study has been evaluated using parameters like precision, recall, F1 measures and execution time. Also, an in-depth comparative analysis of state-of-the-art web page recommendation systems with the recommendation system considered for the study has been done. The results indicate that the proposed clustering-based framework was able to generate superior results than the other classes of algorithms.

UX of AI & PersonalizationResearch Papers

Artificial intelligence in recommender systemsZhang · 2020Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learningmethodsandalgorithms,hasbeennaturallyappliedinthedevelopmentofrecommendersystemstoimproveprediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

UX of AI & PersonalizationResearch Papers

User Preferences for Hybrid ExplanationsPigi Kouki · 2017Hybrid recommender systems combine several different sources of information to generate recommendations. These systems demon- strate improved accuracy compared to single-source recommen- dation strategies. However, hybrid recommendation strategies are inherently more complex than those that use a single source of information, and thus the process of explaining recommendations to users becomes more challenging. In this paper we describe a hybrid recommender system built on a probabilistic programming language, and discuss the benefits and challenges of explaining its recommendations to users. We perform a mixed model statis- tical analysis of user preferences for explanations in this system. Through an online user survey, we evaluate explanations for hybrid algorithms in a variety of text and visual, graph-based formats, that are either novel designs or derived from existing hybrid recom- mender systems.

UX of AI & PersonalizationResearch Papers

Deep Neural Networks for YouTube RecommendationsPaul Covington, Jay Adams et al. · 2016YouTube represents one of the largest scale and most sophis- ticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and fo- cus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a sepa- rate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintain- ing a massive recommendation system with enormous user- facing impact.

UX of AI & PersonalizationResearch Papers

Machine Learning Techniques for Recommender Systems – A Comparative Case AnalysisBinu Thomas · 2011Recommender System (RS) is one of the most popular applications of Artificial Intelligence which attracted researchers all around the world. Many machine learning algorithms are used to develop RSs. Choosing the best machine learning algorithm to provide users with a product or service is the most challenging task in the area of RSs. Now we are witnessing a paradigm shift in the purchase habits of people from in-shop to online resulting in the availability of online information exponentially growing every day. The ever-increasing online information and the number of online users create new avenues in RS. In an online shopping scenario, these systems must be able to recommend relevant items to the users. The RSs have to deal with the huge amount of information by filtering the relevant information based on the analysis made on the inputs made by the users during their online sessions. These systems can recommend appropriate items to users based on their interest and previous preference which can lead to increased sales. The three major techniques used to build a RS are content-based, collaborative based and hybrid-based. This paper presents the various applications of RSs and makes a detailed comparative study of different machine learning approaches used. The methodologies used for identifying research articles for analysis, the merits and demerits of different techniques in RSs and domain-specific applications of these techniques are well explained here with scientific review analysis.

UX of AI & PersonalizationResearch Papers