Goal-based hybrid filtering for user-to-user Personalized Recommendation

Recommendation systems are gaining great importance with e-Learning and multimedia on the internet. It fails in some situations such as new-user profile (cold-start) issue. To overcome this issue, we propose a novel goalbased hybrid approach for user-to-user personalized similarity recommendation and present its performance accuracy. This work also helps to improve collaborative filtering using k-nearest neighbor as neighborhood collaborative filtering (NCF) and content-based filtering as content-based collaborative filtering (CBCF). The purpose of combining k-nn with recommendation approaches is to increase the relevant recommendation accuracy and decrease the new-user profile (cold-start) issue. The proposed goal-based approach associated with nearest neighbors, compare personalized profile preferences and get the similarities between users. The paper discussed research architecture, working of proposed goal-based approach, its experimental steps and initial results.DOI:http://dx.doi.org/10.11591/ijece.v3i3.241

Personalized Recommendations on Twitter based on Explicit User Relationship Modelling

Information overload is a recent phenomenon caused by a regular use of social media platforms among millions of users. Websites such as Twitter seem to be getting increasingly popular, providing a perfect platform for sharing information which can help in the process of modelling users and recommender system research. This research studies information overload and uses twitter user modelling through making use of explicit relationships amongst various users. This paper presents a novel personal profile mechanism that helps in the provision of more accurate recommendations by filtering overloaded information as it gathered from Twitter data. The presented method takes advantage of user explicit relationships on Twitter based on influence rule in order to gain information which is vital in the building of the personal profile of the user. In order to validate this proposed method\u27s usefulness a simple tweet recommendation service was implemented by using content-based recommender system. This has also been evaluated using an offline evaluation process. Our proposed user profiles are compared against other profiles such as the baseline in order to have the proposed method\u27s effectiveness checked. The experiment is implemented based on an experimental number of users

Recognizing Customer Knowledge Level towards Products for Recommendation in Electronic Commerce

A powerful online recommendation system in Electronic Commerce (EC) must know its targeted customers well and employ effective marketing strategies. Market research is a very important way to know the customers well. For high-tech products with great variety such as computers, cellular phones, and digital cameras, customers’ knowledge level towards products may have a decisive influence on their purchase decision. While many online recommendation systems focus on utilizing data mining techniques in user profile and transaction data, this paper presents a method for recognizing customer knowledge level as a preprocess for more effective online recommendation in EC. The method consists of two Back Propagation Networks (BPN) and predicts based on customer characteristics and online navigation behaviors. A simple simulated digital camera EC store case study was conducted and the good preliminary result implies the good potential of the proposed method

Building emergent social networks and group profiles by semantic user preference clustering

This is an electronic version of the paper presented at the International Workshop on Semantic Network Analysis (SNA 2006) at the European Semantic Web Conference (ESWC 2006), held in Budva on 2006This paper presents a novel approach to automatic semantic social network construction based on semantic user preference clustering. Considering a number of users, each of them with an associated ontology-based profile, we propose a strategy that clusters the concepts of the reference ontology according to user preferences of these concepts, and then determines which clusters are more appropriate to the users. The resultant user clusters can be merged into individual group profiles, automatically defining a semantic social network suitable for use in collaborative and recommendation environments.This research was supported by the European Commission (FP6-027685 – MESH), and the Spanish Ministry of Science and Education (TIN2005-06885). The expressed content is the view of the authors but not necessarily the view of the MESH project as a whole

Design and field evaluation of REMPAD: a recommender system supporting group reminiscence therapy

This paper describes a semi-automated web-based system to facilitate digital remi-niscence therapy for patients with mild-to-moderate dementia, enacted in a group setting. The system, REMPAD, uses proactive recommendation technology to profile participants and groups, and offers interactive multimedia content from the Internet to match these profiles. In this paper, we focus on the design of the system to deliver an innovative personalized group reminiscence experience. We take a user-centered design approach to discover and address the design challenges and considerations. A combination of methodologies is used throughout this research study, including exploratory interviews, prototype use case walkthroughs, and field evaluations. The results of the field evaluation indicate high user satisfaction when using the system, and strong tendency towards repeated use in future. These studies provide an insight into the current practices and challenges of group reminiscence therapy, and inform the design of a multimedia recommender system to support facilitators and group therapy participants

User interface patterns in recommendation-empowered content intensive multimedia applications

Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (“recommendations”) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, our work can stimulate future research bridging Recommender Systems, Web Engineering and Interface Design by means of Design Patterns, and highlights new research directions also discussed in the paper

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Towards Serendipity for Content–Based Recommender Systems

Recommender systems are intelligent applications build to predict the rating or preference that a user would give to an item. One of the fundamental recommendation methods in the content-based method that predict ratings by exploiting attributes about the users and items such as users’ profile and textual content of items. A current issue faces by recommender systems based on this method is that the systems seem to recommend too similar items to what users have known. Thus, creating over-specialisation issues, in which a self-referential loop is created that leaves user in their own circle of finding and never get expose to new items. In order for these systems to be of significance used, it is important that not only relevant items been recommender, but the items must be also interesting and serendipitous. Having a serendipitous recommendation let users explore new items that they least expect. This has resulted in the issues of serendipity in recommender systems. However, it is difficult to define serendipity because in recommender system, there is no consensus definition for this term. Most of researchers define serendipity based on their research purposes. From the reviews, majority shows that unexpected as the important aspect in defining serendipity. Thus, in this paper, we aim to formally define the concept of serendipity in recommender systems based on the literature work done. We also reviewed few approaches that apply serendipity in the content-based methods in recommendation. Techniques that used Linked Open Data (LOD) approaches seems to be a good candidate to find relevant, unexpected and novel item in a large dataset.