## 2 Related Work  
### Cold Start Problems  
When the dataset is sparse, recommender systems are difficult to provide high-quality recommendations. A method to alleviate the new user cold start problem for recommender systems applying collaborative filtering was presented by [13]. They proposed a model in combination with similarity techniques and prediction mechanisms for retrieving recommendations. A novel approach for alleviating the cold start problem by imputing missing values into the input matrix was proposed by [14]. Their system combined local learning, attribute selection to optimize the recommendation process. They have evaluated the proposed framework on one synthetic and two real datasets, using four different matrix factorization algorithms. A novel solution for cross-site cold start product recommendation to recommend products from e-commerce websites to users at social networking sites in cold start situations was proposed by [15]. They used the linked users across social networking sites and e-commerce websites as a bridge to map users' social networking features to another feature representation for product recommendation. A hybrid interactive context-aware recommender system applied to the tourism domain was proposed by [1]. The approach combined case-based reasoning and an artificial neural network to overcome the cold start problem for a new user with little prior ratings. The proposed method can suggest a tour to a user with limited knowledge about his preferences and considers the user's preference changing during the recommendation process. A hybrid recommendation model for dealing with the cold start problem, in which item features were learned from a deep learning architecture SDAE from the descriptions of the items retrieved, then using these features to the time SVD++ model was proposed by [3]. The experiments were performed to evaluate the collaborative filtering recommendation model, calculating the RMSE with the movie dataset.  
A novel crowd-enabled framework, called CrowdStart, utilizes crowds' wisdom via crowdsourcing. The intuition behind the CrowdStart framework is based on the conventional expert systems was proposed by [16]. The knowledge of domain experts helps solve complex problems that are difficult to solve with machine-only algorithms. The experimental results show that the crowd workers provide relevant, diverse, reliable, and explainable crowd-based neighbors for the new item. The crowd-based neighbors are helpful for new item recommendations. A niche approach that applies interrelationship mining into item-based collaborative filtering (IBCF) was proposed by [17]. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to enrich the available information on a new item. A joint personalized Markov chains (JPMC) model to address the cold-start issues for implicit feedback recommendation system was proposed by [18]. The research first utilizes user embedding to mine network neighbors. A two-level model based on Markov chains at both user level and user group level is proposed to model user preferences dynamically. Useful user selection criteria based on the items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users was designed by [19]. By exploiting the feedback ratings, users' previous ratings and items' attributes, their research then generates accurate rating predictions for the other unselected users. A user similarity detection engine (USDE) for solving the lack of initial social links for newcomers was proposed by [20]. This paper utilizes users' smart devices enabling the USDE to extract real-world social interactions between users automatically. The USDE uses a user clustering algorithm to identify similar users based on their profiles and then provide more personalized recommendations.