# Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
## 3 Proposed Methodology
This method can be applied to sparse user-product matrices which contain users with small or no rating history at all. At first, the proposed methodology considers users' characteristics and performs a clustering procedure to create groups of similar users. Then, by using the rating history of the users who belong to the same cluster, the methodology extracts the frequent itemsets, i.e., combinations of products that the users have rated positively. The discriminant frequent itemsets of each cluster are extracted afterward, i.e., itemsets that are highly frequent in a cluster of users, but less frequent or infrequent in other clusters of users. The initial user-product matrix is enriched by adding to each user's empty cells the positive ratings of the products based on the discriminant itemsets of the cluster the user belongs to. The proposed methodology is flexible enough and can also be applied to predict products that the user does not like or recommend products that are newly released, and that no purchase history exists. The presented empirical results demonstrate the efficiency and accuracy of the proposed methodology, which, to our knowledge, exhibits the highest precision (0.903) among all the similar methods presented in the literature addressing the cold-start problem (See Table 3). ### Limitations

The proposed methodology relies on the quality of the clustering procedure. Obviously, the higher the clustering quality, the better the performance of the proposed methodology. It is known that similar users can only be selected from the fixed size of cluster members, and in general, a fewer number of similar users can be identified compared with the whole space, thus clustering-based methods still suffer from relatively low accuracy [54]. However, the presented empirical results are achieved by using only simple demographic info for the users (age, gender, occupation, zip). It seems that the performance of the proposed methodology will further increase by using more informative characteristics of the user's/product's behavior and context (e.g., social network information, social tags, etc.). Moreover, the clustering quality can be increased by applying more dedication to each application technique such as biclustering, text categorization, fuzzy \(k\)-means, etc. In addition, the minimum support of extracted discriminant frequent itemsets has a great impact on recommendation quality. It is known that such methodologies suffer from the problem of low support. ### Future Work

In this work, we focused on recommending products that the users might like, since this is the usual goal of CF. Additionally, it has been shown that recommending products that the users might dislike is not quite effective by using standard association rule mining [36]. We are currently investigating the efficiency of the proposed methodology for products that the user dislikes. We are also investigating the efficiency of the proposed methodology to cold-start products, i.e., new products with no purchase history. Moreover, we are investigating the improvement of the performance of the proposed methodology by applying clustering to implicit user data deriving from users' social media activity or profiles. Conceptualization, B.B. and A.P. ; methodology, B.B. ; validation, B.B. and A.P. ; resources, A.P. ; writing, B.B. and A.P. ; visualization, A.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Publicly available datasets were analyzed in this study. This data can be found here: [[https://grouplens.org/datasets/movielens/100k/](https://grouplens.org/datasets/movielens/100k/)]([https://grouplens.org/datasets/movielens/100k/](https://grouplens.org/datasets/movielens/100k/)) accessed on 15 December 2022. The authors would like to thank [PERSON] for his invaluable support during the implementation of the proposed method. The authors declare no conflict of interest. ## References

* [PERSON] et al.