SIMILARITY ENHACEMENT IN TIME-AWARE RECOMMENDER SYSTEMS

Time-aware recommender systems (TARS) are systems that take into account a time factor - the age of the user data. There are three approaches for using a time factor: (1) the user data may be given different weights by their age, (2) it may be treated as a step in a biological process and (3) it may be compared in different time frames to find a significant pattern. This research deals with the latter approach. When dividing the data into several time frames, matching users becomes more difficult - similarity between users that was once identified in the total time frame may disappear when trying to match between them in smaller time frames. The user matching problem is largely affected by the sparsity problem, which is well known in the recommender system literature. Sparsity occurs where the actual interactions between users and data items is much smaller in comparison to the entire collection of possible interactions. The sparsity grows as the data is split into several time frames for comparison. As sparsity grows, matching similar users in different time frames becomes harder, increasing the need for finding relevant neighboring users. Our research suggests a flexible solution for dealing with the similarity limitation of current methods. To overcome the similarity problem, we suggest dividing items into multiple features. Using these features we extract several user interests, which can be compared among users. This comparison results in more user matches than in current TARS

Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem. Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts. Our results show significant improvements in consumption of up to 50\% for both offline and online experiments. We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.Comment: SIGIR 202

Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

Sustainable Transformation of Individuals and Families: Design and Implementation of Holistic Personalised Socially Driven Persuasive Systems

Sustainability is a topic that has matured and has evolved from organisational sustain ability to societal sustainability and more recently to individual sustainability. As an individual is the core, basic component of society, and plays a critical role in societal transformation, there is growing interest and discussions on individual sustainability and wellbeing. Since the publication of Our Common Future – the report commis sioned by the UN General Assembly in tackling environmental and natural resources issues – the concept of ‘sustainable development’ has taken root in firms and govern ments, both in optimising their supply chain and in the planning of the sustainability of the society. However, counterintuitively, the fabric of the society – individuals and families – has been neglected in this journey of understanding their roles in sustaina bility, as well as in the nexus between their decisions and social outcomes. This thesis bridges the gap. Sustainability is a transformative process of improving the quality of lives by balancing various of our life aspects, such as economic, ecological, and societal dimensions. In this process, information systems often take a critical part as an analytical tool, which provides insightful decision support and recommendations based on collected data and information. In contrast to systems employed by corporates and governments, the development of sustainability systems for individuals and families is still in its infancy. Existing systems mostly are only focusing on one aspect of life and prescribe a single dimensional solution, without regard to the contextual and circumstantial complexi ties of life. In this light, this thesis aims to design and implement systems that adopt a holistic approach in understanding users’ individualistic needs, and in synthesising their life status and goals. The vision is to recognise the multifaceted aspirations of the users, and to nudge them toward a lifestyle that is sustainable, practical, and, above all, enjoyable. To realise this vision, the thesis adopts the multimethodological design science approaches (Hevner, March, Park, & Ram, 2004; Nunamaker, Chen, & Purdin, 1991) with the design eval uation methods from Hevner, March, Park, and Ram (2004) to address the challenges. ii First, the thesis defines individual and family sustainability and a set of nine principles named SSHARRPPP (Sustainable, Social, Holistic, Adaptive, Real-time, Real-world, Precise, Personalised, Persuasive). Based on these principles, the thesis develops sus tainable transformative processes that are applied to key activities and can bring fun damental changes for one’s life. From these conceptual and procedural foundations, the thesis designs system architectures and implements four systems as proof of con cepts. They are, namely, the SSHARRPPP Measurement, Shopping, Modelling, and Games. SSHARRPPP systems support individual and family sustainability holistically as they work together seamlessly. SSHARRPPP Measurement and Shopping measure key ac tivities that are performed by individuals and families. Based on the measured data, SSHARRPPP Modelling grasps causal effect relationships of one’s life dimensions and develops models. Lastly, SSHARRPPP Games helps people to stick with sustainable lives by making their journey enjoyable. All systems are designed to educate people to transform their lives. During the research, all of these conceptual, procedural, and sys tem artefacts are validated through publications, presentations and peer-review pro cesses. This thesis fills the gap in individual and family sustainability by bringing understand ing of human nature and systems together. Taken as a whole, it provides holistic un derstanding on sustainable life transformation and benefits researchers in both infor mation systems and sustainability. The thesis also lays the ground for future work in health and self-management, as it provides system solutions by synthesising core ideas from purposes of life and values, various human processes, and mechanisms to trans form our lives. At the practical level, the system architecture and the applications guide the system developers to design and implement systems for the sustainable transfor mation of individuals and families. Importantly, this thesis benefits individuals and families by making their sustainable life transformations holistic