**2. Literature survey**

The recommender systems (RS) are the information filtering systems which deal with the large amount of information that is dynamically generated based on user's preferences, interests, and observed behaviors. These traditional recommender systems fall into three categories. They are collaborative filtering-based RS, content-based RS, and knowledge-based RS.

The collaborative recommender systems are the most popular and widely implemented systems. These systems aggregate ratings from the set of users on the item and recommend it. It also identifies the users who are similar with the user from whom recommendations are to be provided. Resnick et al. developed [3] a system called GroupLens to help people to find articles they are most interested in. Stavrianou and Brun developed [4] an application to recommend products based on the opinions and suggestions written in the online product reviews.

The content-based recommender systems learn the user profile based on the product feature where the user has targeted. Lang developed [5] a system called NewsWeeder which uses the words of the text as the features. Zhou and Luo developed [6] a content-based recommender system that views customer shopping history to recommend the similar products based on the similarity between the product features.

The knowledge-based recommender systems provide the entity suggestions based on the deductions from user's needs and preferences. These systems have the knowledge about how a particular product meets the customer requirement based on the factual data. The user profile is also required to provide good product recommendations to the user. Case-based reasoning (CBR) is a kind of knowledge-based recommender system. Kolodner used [7] CBR to recommend the restaurants based on the user's choice of features. Burke used [8] the FindMe system to recommend the online products. Stefan et al. worked [9] on user log data to mine the product preferences based on the like or dislike information available in the log.

Sentiment-based product recommendations have gained research importance in the recent times. The knowledge discovered in terms of product features and opinions from online product reviews among the category of products are useful to the customer in personalized recommendations. These feature-level sentiments are aggregated to form the product sentiment. Chen and Wang proposed [10] a novel explanation interface that fuses the feature sentiment information into the recommendation content. They also provided the support for multiple products comparison with respect to similarity using the common feature sentiments. Gurini et al. proposed [11] friends recommendation technique in Twitter using a novel weighting function which is called sentiment-volume-objectivity (SVO) that considers both the user interests and sentiments. Xiu et al. proposed [12] a recommender system that recognizes the sentiment expressions from the reviews, quantified with the sentiment strength and appropriately recommend products according to customer needs. Recently, Dong et al. developed [13] a product recommendation strategy that combines both similarity and sentiments to suggest products.

The utilization of ontologies for better product recommendations is an emerging research area. Uzun and Christian developed [14] a semantic extension to FOKUS recommender system. This extension is capable of integrating contextual and semantic information in the recommendations. Hadi Khosravi and Mohamad Ali introduced [15] a semantic recommendation procedure using ontology on online products based on the usage patterns of the customers.

The works on ontology-based recommender systems [14, 15] was neither concentrated on utilizing the depth information of the domain feature nodes from the ontology tree nor on height of the ontology tree. These properties act as supervised weights in improving the sentiment of the feature and thereby help in improving the recommendations.
