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.

Personalisation of content is a frequently used technique intended to improve user engagement and provide more value to users. Systems designed to provide recommendations to users are called recommender systems and are used in many different industries. This study evaluates the potential of personalisation in a media group primarily publishing local news, and studies how information stored by the group may be used for recommending content. Specifically, the study focuses primarily on content-based filtering by article tags and user grouping by demographics. This study first analyses the data stored by a media group to evaluate what information, data structures, and trends have potential use in recommender systems. These insights are then applied in the implementation of recommender systems, leveraging that data to perform personalised recommendations. When evaluating the performance of these recommender systems, it was found that tag-based content selection and demographic grouping each contribute to accurately recommending content, but that neither method is sufficient for providing fully accurate recommendations.

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.

As interest in family history research increases, greater numbers of amateurs are participating in genealogy. However, finding sources that provide useful information on individuals in genealogical research is often an overwhelming task, even for experts. Many tools assist genealogists in their work, including many computer-based systems. Prior to this work, recommender systems had not yet been applied to genealogy, though their ability to navigate patterns in large amounts of data holds great promise for the genealogical domain. We create the Family History Source Recommender System to mimic human behavior in locating sources of genealogical information. The recommender system is seeded with existing source data from the FamilySearch database. The typical recommender systems algorithms are not designed for family history work, so we adjust them to fit the problem. In particular, recommendations are created for deceased individuals, with multiple users being able to consume the same recommendations. Additionally, our similarity computation takes into account as much information about individuals as possible in order to create connections that would otherwise not exist. We use offline n-fold cross-validation to validate the results. The system provides results with high accuracy.

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter face two problems 1) their online social friendships and activities are scattered across SNSs. It is difficult for them to keep track of all their friends and the information about their friends online social activities. 2) they are often overwhelmed by the huge amount of social data (friends updates and other activities). To solve these two problems, this research proposes an approach, named SocConnect. Soc- Connect allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. SocConnect also can recommend unread friend updates to the user based on user previous ratings on activi- ties and friends, using machine learning techniques. The results from one pilot studies show that users like SocConnects functionalities are needed and liked by the users. An evaluation of the effectiveness of several machine learning algorithms demonstrated that , and machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with the network overload.

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter face two problems 1) their online social friendships and activities are scattered across SNSs. It is difficult for them to keep track of all their friends and the information about their friends online social activities. 2) they are often overwhelmed by the huge amount of social data (friends updates and other activities). To solve these two problems, this research proposes an approach, named SocConnect. Soc- Connect allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. SocConnect also can recommend unread friend updates to the user based on user previous ratings on activi- ties and friends, using machine learning techniques. The results from one pilot studies show that users like SocConnects functionalities are needed and liked by the users. An evaluation of the effectiveness of several machine learning algorithms demonstrated that , and machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with the network overload.

Nowadays, successful cooperation and collaboration among developers is crucial to build successful projects in distributed software system development (DSSD). Assigning wrong developers to a specific task not only affects the performance of a component of this task but also affects other components since these projects are composed of dependent components. Another aspect that should be considered when teams are built is the social relationships between the members; disagreements between these members also affect the project team’s performance. These two aspects might cause a project’s failure or delay. Therefore, they are important to consider when teams are created. In this thesis, we developed an Expert Recommender System Framework (ERSF) that assists developers (Active Developers) to find experts who can help them complete or fix the bugs in the code at hand. The ERSF analyzes the developer technical expertise on similar code fragments to the one they need help on assuming that those who have worked on similar fragments might understand and help the Active Developer; also, it analyzes their social relationships with the Active Developer as well as their social activities within the DSSD. Our work is also concerned with improving the system performance and recommendations by tracking the developer communications through our ERSF in order to keep developer profiles up-to-date. Technical expertise and sociality are measured using a combination of technical and social heuristics. The recommender system was tested using scenarios derived from real software development data, and its recommendations compared favourably to recommendations that humans were asked to make in the same scenarios; also, they were compared to the recommendations of the NaiveBayes and other machine learning algorithms. Our experiment results show that ERSF can recommend experts with good to excellent accuracy.

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter face two problems 1) their online social friendships and activities are scattered across SNSs. It is difficult for them to keep track of all their friends and the information about their friends online social activities. 2) they are often overwhelmed by the huge amount of social data (friends updates and other activities). To solve these two problems, this research proposes an approach, named SocConnect. Soc- Connect allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. SocConnect also can recommend unread friend updates to the user based on user previous ratings on activi- ties and friends, using machine learning techniques. The results from one pilot studies show that users like SocConnects functionalities are needed and liked by the users. An evaluation of the effectiveness of several machine learning algorithms demonstrated that , and machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with the network overload.

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter face two problems 1) their online social friendships and activities are scattered across SNSs. It is difficult for them to keep track of all their friends and the information about their friends online social activities. 2) they are often overwhelmed by the huge amount of social data (friends updates and other activities). To solve these two problems, this research proposes an approach, named SocConnect. Soc- Connect allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. SocConnect also can recommend unread friend updates to the user based on user previous ratings on activi- ties and friends, using machine learning techniques. The results from one pilot studies show that users like SocConnects functionalities are needed and liked by the users. An evaluation of the effectiveness of several machine learning algorithms demonstrated that , and machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with the network overload.

WiFi-based indoor localization is emerging as a new positioning technology. In this work, we present our efforts to find the best recommender system based on the indoor location tracks collected from the Bow Valley shopping mall for one week. The time a user spends in a shop is considered as an implicit preference and different mapping algorithms are proposed to map the time to a more realistic rating value. A new distribution error metric is proposed to examine the mapping algorithms. Eleven different recommender systems are built and evaluated in terms of accuracy and execution time. The Slope-One recommender system with a logarithmic mapping algorithm is finally selected with a score of 1.292, distribution error of 0.178 and execution time of 0.39 seconds for ten runs.