A comparison of calibrated and intent-aware recommendations

Calibrated and intent-aware recommendation are recent approaches to recommendation that have apparent similarities. Both try, to a certain extent, to cover the user's interests, as revealed by her user profile. In this paper, we compare them in detail. On two datasets, we show the extent to which intent-aware recommendations are calibrated and the extent to which calibrated recommendations are diverse. We consider two ways of defining a user's interests, one based on item features, the other based on subprofiles of the user's profile. We find that defining interests in terms of subprofiles results in highest precision and the best relevance/diversity trade-off. Along the way, we define a new version of calibrated recommendation and three new evaluation metrics

Subprofile-aware diversification of recommendations

A user of a recommender system is more likely to be satisfied by one or more of the recommendations if each individual recommendation is relevant to her but additionally if the set of recommendations is diverse. The most common approach to recommendation diversification uses re-ranking: the recommender system scores a set of candidate items for relevance to the user; it then re-ranks the candidates so that the subset that it will recommend achieves a balance between relevance and diversity. Ordinarily, we expect a trade-off between relevance and diversity: the diversity of the set of recommendations increases by including items that have lower relevance scores but which are different from the items already in the set. In early work, the diversity of a set of recommendations was given by the average of their distances from one another, according to some semantic distance metric defined on item features such as movie genres. More recent intent-aware diversification methods formulate diversity in terms of coverage and relevance of aspects. The aspects are most commonly defined in terms of item features. By trying to ensure that the aspects of a set of recommended items cover the aspects of the items in the user’s profile, the level of diversity is more personalized. In offline experiments on pre-collected datasets, intent-aware diversification using item features as aspects sometimes defies the relevance/diversity trade-off: there are configurations in which the recommendations exhibits increases in both relevance and diversity. In this paper, we present a new form of intent-aware diversification, which we call SPAD (Subprofile-Aware Diversification), and a variant called RSPAD (Relevance-based SPAD). In SPAD, the aspects are not item features; they are subprofiles of the user’s profile. We present and compare a number of different ways to extract subprofiles from a user’s profile. None of them is defined in terms of item features. Therefore, SPAD is useful even in domains where item features are not available or are of low quality. On three pre-collected datasets from three different domains (movies, music artists and books), we compare SPAD and RSPAD to intent-aware methods in which aspects are item features. We find on these datasets that SPAD and RSPAD suffer even less from the relevance/diversity trade-off: across all three datasets, they increase both relevance and diversity for even more configurations than other approaches to diversification. Moreover, we find that SPAD and RSPAD are the most accurate systems across all three datasets

Subprofile aware diversification of recommendations

A user of a recommender system is more likely to be satisfied by one or more of the recommendations if each individual recommendation is relevant to her but additionally if the set of recommendations is diverse. The most common approach to recommendation diversification uses re-ranking: the recommender system scores a set of candidate items for relevance to the user; it then re-ranks the candidates so that the subset that it will recommend achieves a balance between relevance and diversity. Ordinarily, we expect a trade-off between relevance and diversity: the diversity of the set of recommendations increases by including items that have lower relevance scores but which are different from the items already in the set. In early work, the diversity of a set of recommendations was given by an aggregate of their distances from one another, according to some semantic distance metric defined on item features such as movie genres. More recent intent-aware diversification methods formulate diversity in terms of coverage and relevance of aspects. The aspects are most commonly defined in terms of item features. By trying to ensure that the aspects of a set of recommended items cover the aspects of the items in the user’s profile, the level of diversity is more personalized. In offline experiments on pre-collected datasets, intent-aware diversification using item features as aspects sometimes defies the relevance/diversity trade-off: there are configurations in which the recommendations exhibits increases in both relevance and diversity. In this thesis, we present a new form of intent-aware diversification, which we call SPAD (Subprofile-Aware Diversification). In SPAD and its variants, the aspects are not item features; they are subprofiles of the user’s profile. We present a number of different ways to extract subprofiles from a user’s profile. None of them is defined in terms of item features. Therefore, SPAD and its variants are useful even in domains where item features are not available or are of low quality. On several pre-collected datasets from different domains (movies, music, books, social network), we compare SPAD and its variants to intent-aware methods in which aspects are item features. We also compare them to calibrated recommendations, which are related to intent-aware recommendations. We find on these datasets that SPAD and its variants suffer even less from the relevance/diversity trade-off: across all datasets, they increase both relevance and diversity for even more configurations than other approaches. Moreover, we apply SPAD to the task of automatic playlist continuation (APC), in which relevance is the main goal, not diversity. We find that, even when applied to the task of APC, SPAD increases both relevance and diversity

Recommending Privacy Settings for Internet-of-Things

Privacy concerns have been identified as an important barrier to the growth of IoT. These concerns are exacerbated by the complexity of manually setting privacy preferences for numerous different IoT devices. Hence, there is a demand to solve the following, urgent research question: How can we help users simplify the task of managing privacy settings for IoT devices in a user-friendly manner so that they can make good privacy decisions? To solve this problem in the IoT domain, a more fundamental understanding of the logic behind IoT users’ privacy decisions in different IoT contexts is needed. We, therefore, conducted a series of studies to contextualize the IoT users’ decision-making characteristics and designed a set of privacy-setting interfaces to help them manage their privacy settings in various IoT contexts based on the deeper understanding of users’ privacy decision behaviors. In this dissertation, we first present three studies on recommending privacy settings for different IoT environments, namely general/public IoT, household IoT, and fitness IoT, respectively. We developed and utilized a “data-driven” approach in these three studies—We first use statistical analysis and machine learning techniques on the collected user data to gain the underlying insights of IoT users’ privacy decision behavior and then create a set of “smart” privacy defaults/profiles based on these insights. Finally, we design a set of interfaces to incorporate these privacy default/profiles. Users can apply these smart defaults/profiles by either a single click or by answering a few related questions. The biggest limitation of these three studies is that the proposed interfaces have not been tested, so we do not know what level of complexity (both in terms of the user interface and the in terms of the profiles) is most suitable. Thus, in the last study, we address this limitation by conducting a user study to evaluate the new interfaces of recommending privacy settings for household IoT users. The results show that our proposed user interfaces for setting household IoT privacy settings can improve users’ satisfaction. Our research can benefit IoT users, manufacturers, and researchers, privacy-setting interface designers and anyone who wants to adopt IoT devices by providing interfaces that put their most prominent concerns in the forefront and that make it easier to set settings that match their preferences

Intelligent Tourist Recommender System Focused on Collective Profiles

Group recommendation is complex due to the selection procedure, structure and group conduction could conditioning negatively its effectiveness. Aspects like expectations of its components, the group size, time, communication standards, the previous experience or condition of members could have a negative influence. World Tourism Organization (UNWTO) defines tourism as a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business purposes. These people are called visitors (which may be either tourist or excursionists; resident or non-residents) and tourism has to do with their activities, some of which involve tourism expenditure. International tourism now represents 7% of the world’s exports of goods and services, up from 6% in 2014, as tourism has grown faster than world trade over the past four years. Holidays, recreation and other forms of leisure have been just over half of all international tourist arrivals in 2015 (53% or 632 million). Business and professional purposes accounted for some 14% of all international tourists, another 27% travelled for other reasons such as visiting friends and relatives (VFR), religious reasons and pilgrimages, health treatment. The purpose of visit for the remaining 6% of arrivals was not specified. Nowadays, the greater part of tourists around the world plan their vacation, make reservations or buy services, moreover, they share their experiences through the Internet. In this research is implemented an intelligent system for managing and recommending tourist places to collective profiles, which is able to identify and satisfy preferences of group members

Comparing Context-Aware Recommender Systems in Terms of Accuracy and Diversity: Which Contextual Modeling, Pre-filtering and Post-Filtering Methods Perform the Best

Although the area of Context-Aware Recommender Systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.Politecnico di Bari, Italy; NYU Stern School of Busines

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Representation learning in heterogeneous information networks for user modeling and recommendations

Doctor of PhilosophyDepartment of Computer ScienceWilliam H. HsuCurrent research in the field of recommender systems takes into consideration the interaction between users and items; we call this the homogeneous setting. In most real world systems, however these interactions are heterogeneous, i.e., apart from users and items there are other types of entities present within the system, and the interaction between the users and items occurs in multiple contexts and scenarios. The presence of multiple types of entities within a heterogeneous information network, opens up new interaction modalities for generating recommendations to the users. The key contribution of the proposed dissertation is representation learning in heterogeneous information networks for the recommendations task. Query-based information retrieval is one of the primary ways in which meaningful nuggets of information is retrieved from large amounts of data. Here the query is represented as a user's information need. In a homogeneous setting, in the absence of type and contextual side information, the retrieval context for a user boils down to the user's preferences over observed items. In a heterogeneous setting, information regarding entity types and preference context is available. Thus query-based contextual recommendations are possible in a heterogeneous network. The contextual query could be type-based (e.g., directors, actors, movies, books etc.) or value-based (e.g., based on tag values, genre values such as ``Comedy", ``Romance") or a combination of Types and Values. Exemplar-based information retrieval is another technique for of filtering information, where the objective is to retrieve similar entities based on a set of examples. This dissertation proposes approaches for recommendation tasks in heterogeneous networks, based on these retrieval mechanisms present in traditional information retrieval domain