Memory based algorithms, generally referred as similarity based Collaborative Filtering (CF) algorithm, is one of the most widely accepted approaches to provide service recommendations. It provides personalized and automated suggestions to customers to select variety of products. Memory based algorithms mainly have two kinds of algorithms: User-based and Item-based algorithms. The User-based CF algorithm recommends items by finding similar users. Contrary to User-based CF, an Item-based CF algorithm recommends items by finding similar items. The core of memory based CF technologies is to calculate similarity among users or items. However, due to inherent sparsity, a large number of entries (ratings) in user-item rating matrix are missing. This results in only few available ratings to make prediction for the unknown ratings. This results in poor prediction quality of the CF algorithm. In this paper a hybrid approach is presented that combines user-based CF and item-based CF. It also leverage the biclustering technique to reduce the dimensionality. The biclustering helps to cluster all users/items into several groups. These clusters are then used to measure users/items similarities based on their respective parent groups. To obtain individual prediction, it adopts the user-based and item-based CF schemes based on the computed similarity respectively. Finally it combines the resultant predictions of each model to make final predictions. Interestingly, experiments demonstrated that the proposed approach outperforms the traditional user-based, item-based and some state of the art recommendation approaches in terms of accuracy of prediction and quality of recommendations.
Handicraft is one of the many productive sectors for developing countries. It contributes significantly towards economic growth. This study seeks to investigate consumers’ perception towards purchase of wooden handicraft items through e-commerce platform. The proposed extended technology acceptance model investigates the role of website quality, service and product perception on consumers purchase intention towards wooden handicraft items online. Trust acts as a mediator to study its effect on consumer intention. The effect of website quality, service and product perception was analyzed for the technology acceptance model constructs, namely perceived ease of use and perceived usefulness. A total of 234 respondents were surveyed and data was analyzed using structural equation modelling technique. Service perception and product perception determine perceived usefulness whereas perceived ease of use is determined by website quality and service perception. The results show that trust has positive role in determining consumers’ purchase intention. Website quality, service and product perception determine trust, and they build consumers’ confidence in online shopping. Both seller and website should have effective strategies to build consumers’ trust. The research suggests that sellers can significantly surge consumers’ trust by improvising product and service perception, whereas the website can ensure consumers’ trust by increasing the quality of website and service perception.
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