Sistemas de Recomendação ajudam na personalização de sites na Internet oferecendo conteúdo ou produtos específicos aos usuários. Com dispositivos móveis, aumentou o interesse do usuário em ter recomendações personalizadas de locais para ir de acordo com o seu histórico de navegação e avaliações como restaurantes e pontos turísticos. Para que as recomendações personalizadas por locais sejam mais precisas é necessário contextualizá-las de acordo com o interesse do usuário que caracterizado por locais que ele visitou e por regiões de interesse como moradia, onde trabalha ou mesmo onde passará férias. Várias técnicas de contextualização utilizaram todos os locais que o usuário visitou para geração da recomendação contextual do local, outras técnicas trabalham na arquitetura híbrida. Muitas assumem que é necessário a posição exata do usuário para que as recomendações sejam online, o que muitas vezes não é possível por limitações técnicas ou mesmo indisponibilização do usuário por questões de privacidade. O objetivo principal deste trabalho é geração de recomendações usando multicontextos de forma offline, gerando vários contextos de cada usuário. Os locais são recomendados utilizando apenas dados históricos, sem a localização exata no usuário do momento da recomendação. Para atingir este objetivo foram utilizadas técnicas de clustering para mapeamento e divisão dos contextos em regiões indicando o interesse do usuário gerando a recomendação final dos locais a partir de um método híbrido de recomendação que usa filtragem colaborativa e a recomendação contextual proposta. Os resultados mostraram que a técnica proposta apresenta recomendações melhores do que apenas a recomendação colaborativa pura e, para usuários assíduos, as recomendações são melhores do que as técnicas base usadas para comparação. / Recommender Systems help in web sites personalization, offering specific content or products to users. With mobile devices, user interest in Point-of-Interest (POI) recommendation has increased to receive recommendations about places to go according to your navigation and evaluation history in the web site. POI recommendation are improved by contextualizing according to users interest, based on places to where user has been and on regions of interest such as the region where the user lives, works or the region intends go on vacation. Many contextualization techniques use all places that user visited for generation of POI contextual recommendation. Other techniques use hybrid architectures and many of them assume that is necessary the exact point where the user is for online recommendation and this in not possible always due technical limitations or user privacy. The main objective of this work is the offline generation of recommendations using multicontexts. Places to be recommended use only user historical data, without the user current localization at the moment of recommendation. Several techniques have been used for clustering for mapping and division of contexts in regions, indicating the user interests and, finally, generating the final recommendation using a hybrid method with collaborative filtering and contextual recommendation proposed. The results indicate that the proposed technique builds better recommendations than the pure collaborative filtering technique and for heavy users the proposed technique has better results the baseline technique used for comparison.

Collaboration and sharing of information are the basis of modern social web system. Users in the social web systems are establishing and joining online communities, in order to collectively share their content with a group of people having common topic of interest. Group or community activities have increased exponentially in modern social Web systems. With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this study, we address the problem of recommending communities to individual users. Recommender techniques that are based solely on community affiliation, may fail to find a wide range of proper communities for users when their available data are insufficient. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both user’s personal tag usage and other community members’ tagging patterns in the latent space. We specially focus on the challenging problem of recommending communities to users who have joined very few communities or having no prior community membership. Our evaluation on two heterogeneous datasets shows that our approach can significantly improve the recommendation quality.

One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.

To help online trip planners, some online travel agencies and travel service providers have adopted travel recommender systems. Although these systems are expected to support travelers in complex decision-making processes, they are not used efficiently by travelers due to a lack of confidence in the recommendations they provide. It is important to examine factors that can influence the likelihood of recommendations to be accepted and integrated into decision-making processes. The persuasion literature suggests that people are more likely to accept recommendations from credible and likable sources. It has also been found that technologies can be more credible and likable when they give a variety of social cues that elicit social responses from their human users. Thus, it is argued that enhancing the social aspects of travel recommender systems is important to create more persuasive systems. One approach to enhancing the social presence of recommender systems is to use a virtual agent. Current travel recommender systems use various types of virtual agents. However, it is still not clear how those virtual agents are perceived by travel recommender system users and influence users' system evaluations and interactions with these systems. Consequently, this dissertation aimed to investigate the influence of virtual agents presented in travel recommender systems on system users' perceptions. Specifically, the virtual agents' anthropomorphism as well as similarity and authority cues on system users' perceptions of system credibility and liking were examined. For this purpose, two experiments were conducted. For Study 1, the impacts of anthropomorphism of the virtual agents on users' perceptions of virtual agents as well as recommender systems in terms of credibility and attractiveness/liking were examined. Anthropomorphism was manipulated with visual human appearance and voice output. Study 2 tested the influence of virtual agents? similarity and authority on travel recommender system users' perceptions of virtual agents and system credibility and attractiveness/liking. Similarity and authority of the virtual agent were tested by manipulating nonverbal cues (age and outfit) of the agent. The results showed that the characteristics of virtual agents have some influences on system users' perceptions of virtual agents as well as recommender systems. Specifically, a human-like appearance of the virtual agent is found to positively influence users' perceived attractiveness of the virtual agent while voice outputs were found to enhance users' liking of the system (Study 1). Findings also indicate that RS users' perceptions of virtual agent expertise are increased when virtual agents wear a uniform rather than a casual outfit (Study 2). In addition, system users' perceptions of the virtual agent's credibility are found to have a significant influence on users' perceived credibility and liking of the overall system, which implies an important role of virtual agents in recommender system evaluations. Further, perceived credibility and liking of recommender systems lead to favorable evaluations of the recommendations, which, in turn, increase users' intentions to travel to the recommended destination. Past travel recommender system studies have largely neglected the social role of recommender systems as advice givers. Also, it is not clear whether the specific characteristics of virtual agents presented as a part of the system interface influence system users' perceptions. This dissertation sought to close this knowledge gap. By applying classic interpersonal communication theories to human and system relationships, this dissertation expands the scope of traditional theories used in the context of studying recommender systems. Further, the results of the research presented in this dissertation provide insights for tourism marketing as well as practical implications for travel recommender system design.

Ever since the rapid growth of the Internet, recommender systems have become essential in helping online users to search and retrieve relevant information they need. Just like the situation that people rely heavily on recommendation in their daily decision making processes, online users may identify desired documents more effectively and efficiently through recommendation of other users who exhibit similar interests, and/or through extracting crucial features of the users¡¦ past preferences. Typical recommendation approaches can be classified into collaborative filtering and content-based filtering. Both approaches, however, have their own drawbacks. The purpose of this research is thus to propose a hybrid approach for text recommendations. We combine collaborative input and document content to facilitate the creation of extended content-based user profiles. These profiles are then rearranged with the technique of latent semantic indexing. Two experiments are conducted to verify our proposed approach. The objective of these experiments is to compare the recommendation results from our proposed approach with those from the other two approaches. The results show that our approach is capable of distinguishing different degrees of document preference, and makes appropriate recommendation to users or does not make recommendation to users for uninterested documents. The application of our proposed approach is justified accordingly.

Modern social networking sites offer node-centralized streams that display recent updates from the other nodes in one's network. While such social activity streams are convenient features that help alleviate information overload, they can often become overwhelming themselves, especially high-throughput streams like Twitter’s home timelines. In these cases, recommender systems can help guide users toward the content they will find most important or interesting. However, current efforts to manipulate social activity streams involve hiding updates predicted to be less engaging or reordering them to place new or more engaging content first. These modifications can lead to decreased trust in the system and an inability to consume each update in its chronological context. Instead, I propose a three-tiered approach to displaying recommendations in social activity streams that hides nothing and preserves original context by highlighting updates predicted to be most important and de-emphasizing updates predicted to be least important. This presentation design allows users easily to consume different levels of recommended items chronologically, is able to persuade users to agree with its positive recommendations more than 25% more often than the baseline, and shows no significant loss of perceived accuracy or trust when compared with a filtered stream, possibly even performing better when extreme recommendation errors are intentionally introduced. Numerous directions for future research follow from this work that can shed light on how users react to different recommendation presentation designs and explain how study of an emphasis-based approach might help improve the state of the art.

An individual’s ability to locate the information they desire grows more slowly than the rate at which new information becomes available. Customers are constantly confronted with situations in which they have many options to choose from and need assistance exploring or narrowing down the possibilities. Recommender systems are one tool to help bridge this gap. There are various mechanisms being employed to create recommender systems, but the most common systems fall into two main classes: content-based and collaborative filtering systems. Content-based recommender systems match the textual information of a particular product with the textual information representing the interests of a customer. Collaborative filtering systems use patterns in customer ratings to make recommendations. Both types of recommender systems require significant data resources in the form of a customer’s ratings and product features; hence they are not able to generate high quality recommendations. Hybrid mechanisms have been used by researchers to improve the performance of recommender systems where one can integrate more than one mechanism to overcome the drawbacks of an individual system. The hybrid approach proposed in this thesis is the integration of content and context-based with collaborative filtering, since these are the most successful and widely used mechanisms. This proposed approach will look into the integration of content and context data with rating data using a different mechanism that mainly focuses on boosting a customer’s trust in the recommender system. Researchers have been trying to improve system performance using hybrid approaches, but research is lacking on providing justifications for recommended products. Hence, the proposed approach will mainly focus on providing justifications for recommended products as this plays a crucial role in obtaining the satisfaction and trust of customers. A product’s features and a customer’s context attributes are used to provide justifications. In addition to this, the presentation mechanism needs to be very effective as it has been observed that customers trust more in a system when there are explanations on how the recommended products have been computed and presented. Finally, this proposed recommender system will allow the customer to interact with it in various ways to provide feedback on the recommendations and justifications. Overall, this integration will be very useful in achieving a stronger correlation between the customers and products. Experimental results clearly showed that the majority of the participants prefer to have recommendations with their justifications and they received valuable recommendations on which they could trust.

In this dissertation, the issues of the increased awareness of energy use are considered. Energy technologies are continuously improved by energy retailers and academic researchers. The Smart Grid are soon customary as part of the energy domain. But in order to improve energy efficiency the change must come from the consumers. Consumers should be active decision makers in the Smart Grid domain and therefor a Recommender system suits the Smart Grid and enables customers. Customers will not use energy in the way energy retailers, and politicians advocates instead they will do what fits them. By investigating how a Recommender can be built in the Smart Grid we focus on parameters and information that supports the costumers and enables positive change. An investigation of what customers perceive as relevant is pursued as well as how relevancy can adjust the system. A conceptual model of how to build a Recommender is rendered through a literature review, a group interview and a questionnaire.

Recommendation systems have become extremely common in recent years due to the ubiquity of information across various applications. Online entertainment (e.g., Netflix), E-commerce (e.g., Amazon, Ebay) and publishing services such as Google News are all examples of services which use recommender systems. Recommendation systems are rapidly evolving in these years, but these methods have fallen short in coping with several emerging trends such as likes or votes on reviews. In this work we have proposed a new method based on collaborative filtering by considering other users' feedback on each review. To validate our approach we have used Yelp data set with more than 335,000 product and service category ratings and 70,817 real users. We present our results using comparative analysis with other well-known recommendation systems for particular categories of users and items. / Graduate / 0984 / 0800 / yheshmat@uvic.ca

Thesis (M.A.). / Written for the Dept. of Art History and Communication Studies [Communications Graduate Program]. Title from title page of PDF (viewed 2008/05/12). Includes bibliographical references.