Like-tasted user groups to predict ratings in recommender systems

International audienceRecommendation Systems have gained the intention of many researchers due to the growth of the business of personalizing, sorting and suggesting products to customers. Most of rating prediction in recommendation systems are based on customer preferences or on the historical behavior of similar customers. The similarity between customers is generally measured by the number of times customers liked or disliked the same item. Given the huge number and the variety of items, many customers cannot be considered as similar, as they did not evaluate the same items, even if they have similar tastes. This paper presents a new method of rating prediction in recommendation systems. The proposed method starts by identifying the taste directions or the interest centers based on the users' demographic information combined with their previous evaluations. Thus, it uses the Principal Component Analysis (PCA) to retrieve the major taste orientations. According to these orientations, user groups are created. Then, for each group, it generates a prediction model, that will be used to predict unknown rates of users within the corresponding group. In order to assess the accuracy of the proposed method, we compare its results with four baseline methods, namely: RegSVD, BiasedMF, SVD++ and MudRecS. Results prove that the proposed algorithm is more accurate than the base-line algorithms

Ensembles of choice-based models for recommender systems

In this thesis, we focused on three main paradigms: Recommender Systems, Decision Making, and Ensembles. The work is structured as follows. First, the thesis analyzes the potential of choice-based models. The motivation behind this was based on the idea of applying sound decisionmaking paradigms, such as choice and utility theory, in the field of Recommender Systems. Second, this research analyzes the cognitive process underlying choice behavior. On the one hand, neural and gaze activity were recorded experimentally from different subjects performing a choice task in a Web Interface. On the other hand, cognitive were fitted using rational, emotional, and attentional features. Finally, the work explores the hybridization of choice-based models with ensembles. The goal is to take the best of the two worlds: transparency and performance. Two main methods were analyzed to build optimal choice-based ensembles: uninformed and informed. First one, two strategies were evaluated: 1-Learner and N-Learners ensembles. Second one, we relied on three types of prior information: (1) High diversity, (2) Low error prediction (MSE), (3) and Low crowd error

soMLier: A South African Wine Recommender System

Though several commercial wine recommender systems exist, they are largely tailored to consumers outside of South Africa (SA). Consequently, these systems are of limited use to novice wine consumers in SA. To address this, the aim of this research is to develop a system for South African consumers that yields high-quality wine recommendations, maximises the accuracy of predicted ratings for those recommendations and provides insights into why those suggestions were made. To achieve this, a hybrid system “soMLier” (pronounced “sommelier”) is built in this thesis that makes use of two datasets. Firstly, a database containing several attributes of South African wines such as the chemical composition, style, aroma, price and description was supplied by wine.co.za (a SA wine retailer). Secondly, for each wine in that database, the numeric 5-star ratings and textual reviews made by users worldwide were further scraped from Vivino.com to serve as a dataset of user preferences. Together, these are used to develop and compare several systems, the most optimal of which are combined in the final system. Item-based collaborative filtering methods are investigated first along with model-based techniques (such as matrix factorisation and neural networks) when applied to the user rating dataset to generate wine recommendations through the ranking of rating predictions. Respectively, these methods are determined to excel at generating lists of relevant wine recommendations and producing accurate corresponding predicted ratings. Next, the wine attribute data is used to explore the efficacy of content-based systems. Numeric features (such as price) are compared along with categorical features (such as style) using various distance measures and the relationships between the textual descriptions of the wines are determined using natural language processing methods. These methods are found to be most appropriate for explaining wine recommendations. Hence, the final hybrid system makes use of collaborative filtering to generate recommendations, matrix factorisation to predict user ratings, and content-based techniques to rationalise the wine suggestions made. This thesis contributes the “soMLier” system that is of specific use to SA wine consumers as it bridges the gap between the technologies used by highly-developed existing systems and the SA wine market. Though this final system would benefit from more explicit user data to establish a richer model of user preferences, it can ultimately assist consumers in exploring unfamiliar wines, discovering wines they will likely enjoy, and understanding their preferences of SA wine

DIGITAL WINE: HOW PLATFORMS AND ALGORITHMS WILL RESHAPE THE WINE INDUSTRY

La tesi si propone di analizzare come la digitalizzazione e gli approcci basati sui dati, in particolare quelli che sfruttano l'intelligenza artificiale, stiano impattando il settore vitivinicolo e facendo emergere modelli nuovi di business. Quest'ultimo aspetto sarà approfondito tramite due casi studio di piattaforme digitali che, attraverso approcci diversi, stanno contribuendo a generare un ecosistema digitale virtuoso, con potenziali benefici per tutta la catena del valore a livello di settore.The thesis aims to analyze how digitalization and data-driven approaches, in particular those that leverage artificial intelligence, are impacting the wine industry and generating new business models. The latter aspect will be explored through two case studies of digital platforms which, through different approaches, are helping to generate a virtuous digital ecosystem, with potential benefits for the entire value chain at the industry level

Music recommender systems. Proof of concept

Data overload is a well-known problem due to the availability of big on-line distributed databases. While providing a wealth of information the difficulties to find the sought data and the necessary time spent in the search call for technological solutions. Classical search engines alleviate this problem and at the same time have transformed the way people access to the information they are interested in. On the other hand, Internet also has changed the music consuming habits around the world. It is possible to find almost every recorded song or music piece. Over the last years music streaming platforms like Spotify, Apple Music or Amazon Music have contributed to a substantial change of users’ listening habits and the way music is commercialized and distributed. On-demand music platforms offer their users a huge catalogue so they can do a quick search and listen what they want or build up their personal library. In this context Music Recommender Systems may help users to discover music that match their tastes. Therefore music recommender systems are a powerful tool to make the most of an immense catalogue, impossible to be fully known by a human. This project aims at testing different music recommendation approaches applied to the particular case of users playlists. Several recommender alternatives were designed and evaluated: collaborative filtering systems, content-based systems and hybrid recommender systems that combine both techniques. Two systems are proposed. One system is content-based and uses correlation between tracks characterized by high-level descriptors and the other is an hybrid recommender that first apply a collaborative method to filter the database and then computes the final recommendation using Gaussian Mixture Models. Recommendations were evaluated using objective metrics and human evaluations, obtaining positive results.Ingeniería de Sistemas Audiovisuale

Mediating chance encounters through opportunistic social matching

Chance encounters, the unintended meeting between people unfamiliar with each other, serve as an important social lubricant helping people to create new social ties, such as making new friends or finding an activity, study or collaboration partner. Unfortunately, social barriers often prevent chance encounters in environments where people do not know each other and people have to rely on serendipity to meet or be introduced to interesting people around them. Little is known about the underlying dynamics of chance encounters and how systems could utilize contextual data to mediate chance encounters. This dissertation addresses this gap in research literature by exploring the design space of opportunistic social matching systems that aim to introduce relevant people to each other in the opportune moment and the opportune place in order to encourage face-to-face interaction. A theoretical framework of relational, social and personal context as predictors of encounter opportunities is proposed and validated through a mixed method approach using interviews, experience sampling and a field study of a design prototype. Key contributions of the field interview study (n=58) include novel context-aware social matching concepts such as: sociability of others as an indicator of opportune social context; activity involvement as an indicator of opportune personal context; and contextual rarity as an indicator of opportune relational context. The following study combining Experience Sampling Method (ESM) and participant interviews extends prior research on social matching by providing an empirical foundation for the design of opportunistic social matching systems. A generalized linear mixed model analysis (n=1781) shows that personal context (mood and busyness) together with the sociability of others nearby are the strongest predictors of people’s interest in a social match. Interview findings provide novel approaches on how to operationalize relational context based on social network rarity and discoverable rarity. Moreover, insights from this study highlight that additional meta-information about user interests is needed to operationalize relational context, such as users’ passion level for an interest and their skill levels for an activity. Based on these findings, the novel design concept of passive context-awareness for social matching is put forward. In the last study, Encount’r, an instantiation of an opportunistic social matching system, is designed and evaluated through a field study and participant interviews. A large-scale user profiling survey provides baseline rarity measures to operationalize relational context using rarity, passion levels, skills, needs, and offers. Findings show that attribute type, computed attribute rarity, self-reported passion levels for interest, and response time are associated with people’s interest in a match opportunity. Moreover, this study extends prior work by showing how the concept of passive context-awareness for opportunistic social matching is promising. Collectively, contributions of this work include a theoretical framework encompassing relational, social, and personal context; new innovative concepts to operationalize each of these aspects for opportunistic social matching; and field-tested design affordances for opportunistic social matching systems. This is important because opportunistic social matching systems can lead to new social ties and improved social capital

Getting more out of wine: wine experts, wine apps and sensory science

How do consumers decide which wines to buy from the bewildering range on offer to them? Who should they turn to for advice? The answers to these questions are of interest not just to consumers but also to producers and wine merchants who hope to influence consumers’ choices and develop their interests in wine. At one time, consumers looked to the points awarded by authoritative wine critics but increasingly, they use wine apps to extend their wine choices. Reliance on digital technology is meant to replace reliance on expert wine tasters whose judgments can be questioned or whose verdicts on what count as good quality wines may not line-up with the tastes and preferences of ordinary wine consumers. Wine apps’ recommendations based on the wisdom of the crowd favour what most people like but offer little insight into why they like it. It is here that sensory science can play a role in identifying the drivers of liking; however liking should be distinguished from quality. Wine experts aim to identify wine quality; wine apps mostly aim at average liking. To get more out of wine consumers need a way to go beyond liking