ناجي أحمد البطاينه

ASUجامعة العلوم التطبيقية الخاصة - الصفحة الرئيسيةكلية تكنولوجيا المعلوماتناجي أحمد البطاينهالأبحاث

الأبحاث

Discriminate2Rec: Negation-based dynamic discriminative interest-based preference learning for semantics-aware content-based recommendation

Authors :Naji Ahmad Albatayneh, Khairil Imran Ghauth, Fang-Fang Chua

Abstract :Content-based recommender systems (CBRSs) have shown to be highly effective for enhancing user experience in various problems and application domains. Most existing CBRSs employ static preference learning techniques, neglecting that user preferences constantly change over time and are attributed to only a set of certain attributes at a certain period of time due to the existence of temporal dynamics, as only certain attributes of highly preferred items actually reflect user positive interests, while many others might have neutral or even negative influence on user preferences regardless their semantic similarity. Lacking the efficiency in handling these problems poses semantical and temporal incoherence in user profiles, which significantly affects the recommendation accuracy. This paper presents Discriminate2Rec, a three-stage preference learning model that discriminates between items’ attributes based on their influence on user temporal preferences to improve temporal and semantical attribute-level profile coherence for more accurate recommendation. Evaluation is made on three real-world data sets. The results demonstrate the effectiveness of Discriminate2Rec in outperforming state-of-the-art approaches in terms of recommendation accuracy. More in detail, the results also demonstrate that: (1) learning user profiles based on the discrimination between items’ attributes improves the attribute-level profile coherence; (2) modelling the negation in user temporal preferences allows Discriminate2Rec to recommend items whose content are semantically similar to user positive interests to which user negative interests are semantically irrelevant; (3) this novel combination significantly improves temporal and semantical attribute-level profile coherence, and maximises the utilisation of signal, thus significantly improves the recommendation accuracy consistently over time

Keywords :Content-based filtering, semantics-aware recommender systems, temporal dynamics, user discriminative interests, preference learning, profile coherence

Visit

Utilizing Learners’ Negative Ratings in Semantic Content based Recommender System for e Learning Forum

Authors : Naji Ahmad Albatayneh, Khairil Imran Ghauth, Fang-Fang Chua

Abstract :Nowadays, most of e-learning systems embody online discussion forums as a medium for collaborative learning that supports knowledge sharing and information exchanging between learners. The exponential growth of the available shared information in e-learning online discussion forums has caused a difficulty for learners in discovering interesting information. This paper introduces a novel recommendation architecture that is able to recommend interesting post messages to the learners in an e-learning online discussion forum based on a semantic content-based filtering and learners’ negative ratings. We evaluated the proposed e-learning recommender system against exiting e-learning recommender systems that use similar filtering techniques in terms of recommendation accuracy and learners’ performance. The obtained experimental results show that the proposed e-learning recommender system outperforms other similar e-learning recommender systems that use non-semantic content-based filtering technique (CB), non-semantic content-based filtering technique with learners’ negative ratings (CB-NR), semantic content-based filtering technique (SCB), with respect to system accuracy of about 57%, 28%, and 25%, respectively. Furthermore, the obtained results also show that the learning performance has been increased by at least 9.84% for the learners whom are supported by recommendations based on the proposed technique as compared to other similar recommendation techniques

Keywords: E-learning recommender system, E-learning discussion forum, Content-based filtering, Learners’ negative ratings, Latent semantic analysis

Visit

A Semantic Content-based Forum Recommender System Architecture Based on Content-based Filtering and Latent Semantic Analysis

Authors :Naji Ahmad Albatayneh, Khairil Imran Ghauth, Fang-Fang Chua

Abstract :The rapidly increasing popularity of social computing has encouraged Internet users to interact with online discussion forums to discuss various topics. Online discussion forums have been used as a medium for collaborative learning that supports knowledge sharing and information exchanging between users. One of the serious problems of such environments is high volume of shared data that causes a difficulty for users to locate relevant content to their preferences. In this paper, we propose an architecture of a forum recommender system that recommends relevant post messages to users based on content-based filtering and latent semantic analysis which in turn will increase the dynamism of online forums, help users to discover relevant post messages, and prevent them from redundant post messages as well as bad content post messages

Keywords : Recommender system, Content-based filtering, Latent Semantic Analysis

Visit