Most online shops apply recommender systems, i.e. software agents that elicit the users’ preferences and interests with the purpose to make product recommendations. Many of these systems suffer from the new user cold start problem which occurs when no transaction history is available for the particular new prospective buyer. External data from social networking sites, like Facebook, seem promis...

A recommender system is a commonly used technique to improve user experience in e-commerce applications. One of the popular recommender methods is Matrix Factorization (MF) that learns the latent profile of both users and items. However, if the historical ratings are not available, the latent profile will draw from a zero-mean Gaussian prior, resulting in uninformative recommendations. To deal ...

Due to the rapid growth of internet, a useful technology named recommender system (RS) become an effective application to make recommendations to users, nowadays, many collaborative recommender systems (CRS) have succeeded in some fields like movies and music web applications; however, there are also some ways for them to be a more effective RS. This paper introduces a new item-based collaborat...

This article analyzes the influence of the ignition retardation on the fuel consumption, the cumulative tailpipe hydrocarbon emissions, and the temperature inside the three-way catalytic converter in a gasoline direct injection engine operated under idling conditions. A dedicated cylinder-individual, model-based, multivariable controller was used in experiments in order to isolate the effect of...

Collaborative filtering inherently suffers from the data sparsity and cold start problems. Social networks have been shown useful to help alleviate these issues. However, social connections may not be available in many real systems, whereas implicit item relationships are lack of study. In this paper, we propose a novel matrix factorization model by taking into account implicit item relationshi...

In online display advertising, state-of-the-art Click Through Rate(CTR) prediction algorithms rely heavily on historical information, and they work poorly on growing number of new ads without any historical information. This is known as the the cold start problem. For image ads, current stateof-the-art systems use handcrafted image features such as multimedia features and SIFT features to captu...

Relational association rules reveal patterns hidden in multiple tables. Existing rules are usually evaluated through two measures, namely support and confidence. However, these two measures may not be enough to describe the strength of a rule. In this paper, we introduce granular association rules with four measures to reveal connections between granules in two universes, and propose three algo...

We describe a novel approach that integrates user-generated tags with standard Matrix Factorization to allow users to interactively control recommendations. The tag information is incorporated during the learning phase and relates to the automatically derived latent factors. Thus, the system can change an item’s score whenever the user adjusts a tag’s weight. We implemented a prototype and perf...

Recent developments in recommendation algorithms have focused on integrating Linked Open Data to augment traditional algorithms with background knowledge. These developments recognize that the integration of Linked Open Data may offer better performance, particularly in cold start cases. In this paper, we explore if and how a specific type of Linked Open Data, namely hierarchical knowledge, may...

In our current research we use CBR to identify the emotional state of an user during her interaction with a recommender system by analysing pictures of her momentary facial expression. In a previous work [2] we introduced PhotoMood, a CBR system that uses gestures to identify emotions from the user face self-pictures, and presented preliminary experiments analysing only the external mouth conto...