Recommender system focuses on techniques that could predict user interest and give assistance while the user interacts with the Web in finding relevant information. It attempt to make sense of the data generated by his past interaction and predict in future choices. The focus of research in the area of recommender system has been on accuracy in the past decade, but the trend is changing with an increasing interest in this area of research. This paper is an attempt to provide an overview of the state of the art in new dimension of recommender system research. Novelty and serendipity refers to the search of finding something new by a user while browsing world wide web. Traditional recommender system algorithm focuses on accuracy that tries to compare accuracy with past data which limits the scope of novelty and serendipity to a great extent. Novelty pertains to giving something new which the user have not accesses before but similar in taste while serendipity is a chance discovery that could be really beneficial for a user at certain times. This paper will present an outlook on the existing research carried out in this area, their specialized focus with respect to an applicative objectives and the need for a more comprehensive new entrant in this sphere in the light of the current scenario. The paper will also present a novel methodology based on temporal parameters to include the novelty and serendipity in recommender system. In the end, the paper will be concluded by listing some challenges and future trends in this research area.