Abstract:
Recommender system is a particular type of intelligent system which predicts the rating or
preference of an item that a user would like to give. It is based on information filtering process
which simply filter out the undesirable material. It provides only most relevant products
or information in which a particular user is interested through personalized recommendation.
Currently, it is playing an important role in numerous e-commerce services, social networking
applications and recommending products to people such as books, movies, news, garments,
research articles, and web pages. The recommender system suffer from cold start
problem of different degrees where no rating records or few ones exist for newly coming
items or users into the system. Many recommender systems have been proposed in literature
but their efficiency reduces in terms of accuracy when cold start items or users comes
into the system. Thus, there is a need of recommender technique which can generate more
efficient and accurate recommendations under cold start problem.
In this thesis, an accurate hybrid recommender technique is proposed. The proposed technique
is based on the concept of word embedding to produce the distributed representation of
items description along with natural language processing technique to get higher representation
for an item. The content embedding are incorporated in memory based collaborative
filtering technique. The proposed system is capable to generate more accurate and efficient
recommendations under cold start item problem.
The content embedding based feedforward neural network technique is also proposed in
this thesis. The technique is built on the model based approach which utilizes the neural
network. The neural network is used as a profile learner which identifies the hidden trends
and patterns to build a model based on auxiliary information along with user ratings and it
predict the rating for cold start items more accurately.
The experimental results and quantitative comparison with other state-of the-art techniques
is provided which demonstrate the importance of proposed techniques.
