Nowadays customers would rather buy their needs online than visiting a retail store because of many reasons such as saving time. Therefore, in order to increase efficiency of online shopping websites, many companies have invested in researches toward prediction of users purchases and recommendation sys tems that may help and motivate a user to buy products that he may be interested in. However, most efforts in this area has been around classification and predictions based on users interests in specific types of products. In this paper, we have studied efficiency of numerous algorithms toward building a classification model to predict the probability of a complete purchase by users only based on their behavior models in the system and regardless of their interest. Therefore, we experimented accuracy of difl'erent algorithms and proposed a novel classification model that is able to predict whether a user will be interested in buying a certain set of products that are placed in the online shopping cart or not.
News recommender systems efficiently handle the overwhelming number of news articles, simplify navigations, and retrieve relevant information. Many conventional news recommender systems use collaborative filtering to make recommendations based on the behavior of users in the system. In this approach, the introduction of new users or new items can cause the cold start problem, as there will be insufficient data on these new entries for the collaborative filtering to draw any inferences for new users or items. Contentbased news recommender systems emerged to address the cold start problem. However, many content-based news recommender systems consider documents as a bag-of-words neglecting the hidden themes of the news articles. In this paper, we propose a news recommender system leveraging topic models and time spent on each article. We build an automated recommender system that is able to filter news articles and make recommendations based on users' preferences. We use topic models to identify the thematic structure of the corpus. These themes are incorporated into a content-based recommender system to filter news articles that contain themes that are of less interest to users and to recommend articles that are thematically similar to users' preferences. Our experimental studies show that utilizing topic modeling and spent time on a single article can outperform the state of the arts recommendation techniques. The resulting recommender system based on the proposed method is currently operational at The Globe and Mail (http://www.theglobeandmail.com/).
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