Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Movie Recommender System using Collaborative Filtering

Recommender systems have been a crucial research area in late years. It’s a tool that provide recommendation which may be helpful to the user to select item of their interest among thousand other items. In this paper we have given a brief description of collaborative and content based filtering. It contains difference between the two and the flaws they contain. It also reviews the literature of recommender system. The intent of the paper is to study the working of collaborative filtering method using film-trust dataset. The results obtained present a list of recommendation

Semantic-enhanced hybrid recommender systems for personalised e-Government services

University of Technology, Sydney. Faculty of Engineering and Information Technology.E-Government is becoming ever more active in terms of improving the provision of services to citizens from a citizen-centred perspective, in which online services and information are delivered to citizens on a personalised basis. Some developed governments have started to offer personalised services through their official portals. However, the personalised services that are offered are mostly limited to static customisation and are therefore far from achieving effective citizen-centred e-Government services. Furthermore, delivering personalised online services that match the different needs and interests of government users is a challenge for e-Government, specifically in connection with the increasing information and services that are offered through the medium of government portals. Therefore, more advanced and intelligent e-Government systems are desirable. Personalisation techniques, particularly in the form of recommender systems, are promising to provide better solutions to support the development of personalisation in e-Government services. Furthermore, semantic enhanced recommender systems can better support citizen-centred e-Government services and enhance recommendation accuracy. The success of semantic enhanced hybrid recommendation approaches and the citizen-centric initiative of e-Government have fostered the idea of developing personalised e-Government recommendation service systems using semantic enhanced hybrid recommender systems. Accordingly, the effectiveness of utilising the semantic knowledge of e-Government services to enhance the recommendation quality of offered services is addressed in this thesis. This thesis makes five significant contributions to the area of e-Government personalised recommendation services. These contributions are summarised as follows: (i) the thesis first proposes a general framework for offering personalised e-Government services from a citizen-centred perspective based on the available user profiles information and semantic knowledge of a specific e-Government domain of interest; (ii) based on this general framework, a personalised e-Government tourism service recommendation framework is also proposed and considered as a target domain in this research study; (iii) new semantic enhanced hybrid recommendation approaches are developed to support the implementation of the recommendation generator engines of the proposed e-Government frameworks. The recommendation generator engines represent the core components of the proposed frameworks; (iv) new semantic similarity measures based on semantic knowledge of a target domain ontology are proposed to effectively evaluate the similarity between e-Government service items. The new semantic similarity measures are incorporated within the proposed hybrid approaches to improve the quality and accuracy of recommendations and to overcome the limitations of existing hybrid recommendation approaches; and (v) a switching semantic enhanced hybrid recommendation system is further proposed to enhance the overall quality of recommendation, address the sparsity, the cold-start user and item problems. Experimental evaluations of the proposed semantic enhanced hybrid recommendation approaches and switching system, on a real world tourism dataset, show promising results against state-of-the-art recommendation approaches in terms of the quality of recommendations, capacity to alleviate the sparsity, cold-start item and user problems

Efficient Behavior Prediction Based on User Events

In 2020 we have witnessed the dawn of machine learning enabled user experience. Now we can predict how users will use an application. Research progressed beyond recommendations, and we are ready to predict user events. Whenever a human interacts with a system, user events are dispatched. They can be as simple as a mouse click on a menu item or more complex, such as buying a product from an eCommerce site. Collaborative filtering (CF) has proven to be an excellent approach to predict events. Because each user can generate many events, this inevitably leads to a vast number of events in a dataset. Unfortunately, the operation time of CF increases exponentially with the increase of data-points. This paper presents a generalized approach to reduce the dataset’s size without compromising prediction accuracy. Our solution transformed a dataset containing over 20 million user events (20,692,840 rows) into a sparse matrix in about 7 minutes (434.08 s). We have used this matrix to train a neural network to accurately predict user events

Resolving Cold Start Problem Using User Demographics and Machine Learning Techniques for Movie Recommender Systems

There is a substantial increase in demand for recommender systems which have applications in a variety of domains. The goal of recommendations is to provide relevant choices to users. In practice, there are multiple methodologies in which recommendations take place like Collaborative Filtering (CF), Content-based filtering and Hybrid approach. For this paper, we will consider these approaches to be traditional approaches. The advantages of these approaches are in their design, functionality and efficiency. However, they do suffer from some major problems such as data sparsity, scalability and cold start to name a few. Among these problems, cold start is an intriguing area which has been plaguing recommender systems. Cold start problem occurs when the recommender system is not able to recommend new users/items since there is data sparsity. Researchers have formulated innovative techniques to alleviate cold start and the existing research conducted in this area is tremendous since the problem materializes in different use cases. Cold start is categorized into three problems. The first problem is when new users needs product recommendations from the system. The second problem is when new products listed in the system need to be recommended to existing users. The last problem is when new users and new products are present and the recommender engine needs to generate relevant recommendations. In this thesis, we concentrate on the first problem, where a user who is completely new to the system needs quality recommendations. We use a movie recommendation platform as our use case to analyze user demographics and find similarities between existing and new users to produce relevant recommendations

Developing Hybrid-Based Recommender System with Naïve Bayes Optimization to Increase Prediction Efficiency

Commerce and entertainment world today have shifted to the digital platforms where customer preferences are suggested by recommender systems. Recommendations have been made using a variety of methods such as content-based, collaborative filtering-based or their hybrids. Collaborative systems are common recommenders, which use similar users’ preferences. They however have issues such as data sparsity, cold start problem and lack of scalability. When a small percentage of users express their preferences, data becomes highly sparse, thus affecting quality of recommendations. New users or items with no preferences, forms cold start issues affecting recommendations. High amount of sparse data affects how the user-item matrices are formed thus affecting the overall recommendation results. How to handle data input in the recommender engine while reducing data sparsity and increase its potential to scale up is proposed. This paper proposed development of hybrid model with data optimization using a Naïve Bayes classifier, with an aim of reducing data sparsity problem and a blend of collaborative filtering model and association rule mining-based ensembles, for recommending items with an aim of improving their predictions. Machine learning using python on Jupyter notebook was used to develop the hybrid. The models were tested using MovieLens 100k and 1M datasets. We demonstrate the final recommendations of the hybrid having new top ten highly rated movies with 68% approved recommendations. We confirm new items suggested to the active user(s) while less sparse data was input and an improved scaling up of collaborative filtering model, thus improving model efficacy and better predictions

An effective recommender system by unifying user and item trust information for B2B applications

© 2015 Elsevier Inc. Although Collaborative Filtering (CF)-based recommender systems have received great success in a variety of applications, they still under-perform and are unable to provide accurate recommendations when users and items have few ratings, resulting in reduced coverage. To overcome these limitations, we propose an effective hybrid user-item trust-based (HUIT) recommendation approach in this paper that fuses the users' and items' implicit trust information. We have also considered and computed user and item global reputations into this approach. This approach allows the recommender system to make an increased number of accurate predictions, especially in circumstances where users and items have few ratings. Experiments on four real-world datasets, particularly a business-to-business (B2B) case study, show that the proposed HUIT recommendation approach significantly outperforms state-of-the-art recommendation algorithms in terms of recommendation accuracy and coverage, as well as significantly alleviating data sparsity, cold-start user and cold-start item problems