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Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user's preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely RoundRobin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

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<p class="0abstract">The issue of lacking reference books in braille in most public building is crucial, especially public places like libraries, museum and others. The visual impairment or blind people is not getting the information like we normal vision do. Therefore, a multi languages reading device for visually impaired is built and designed to overcome the limitation of reference books in public places. Some research regarding current product available is done to develop a better reading device. This reading device is an improvement from previous project which only focuses on single language which is not suitable for public places. This reading device will take a picture of the book using 5MP Pi camera, Google Vision API will extract the text, and Google Translation API will detect the language and translated to desired language based on push buttons input by user. Google Text-to-Speech will convert the text to speech and the device will read out aloud in through audio output like speaker or headphones.   A few testings have been made to test the functionality and accuracy of the reading device. The testings are functionality, performance test and usability test. The reading device passed most of the testing and get a score of 91.7/100 which is an excellent (A) rating<strong>.</strong></p>