# Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
## 3 Proposed Methodology
The results shown for the proposed methodology concern both the _out-of-matrix_ and the _in-matrix prediction_, while those for similar methods only the _in-matrix prediction_. The proposed methodology outperforms similar methods with respect to all the measures, even for the _out-of-matrix prediction_ setup. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Precision** & **Recall** & **F1-Measure** \\ \hline
**Proposed method (_out-of-matrix_)** & **0.895** & **0.463** & **0.61** \\
**Proposed method (_in-matrix_, 30–90\%)** & **0.903–0.900** & **0.444–0.445** & **0.595–0.595** \\ \cline{2-4} [PERSON] et al. (2020) ([PERSON] et al., 2013) & 0.035 & 0.121 & 0.052 \\ [PERSON], [PERSON] et al. (2013) ([PERSON] et al., 2016) ([PERSON] et al., 2016) & 0.651 & - & - \\ \cline{2-4} [PERSON], [PERSON] et al. (2016) ([PERSON] et al., 2016) & 0.786 & 0.287 & 0.367 \\ \cline{2-4} [PERSON] and [PERSON] (2009) ([PERSON] et al., 2016) & 0.666 & 0.239 & 0.311 \\ \cline{2-4} [PERSON], [PERSON] (2013) & 0.37–0.57 & 0.25–1 & - \\ \cline{2-4} [PERSON] and [PERSON] (2010) ([PERSON] and [PERSON], 2010) & - & - & 0.308 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison to similar methods. ## 5 Discussion

We presented a methodology that can handle efficiently sparsity problems in recommender systems and especially can provide product recommendations to cold-start users, i.e., new users with no rating history. We addressed the sparsity and cold-start problems by applying a combination of clustering and association rule mining algorithms. First, we apply a clustering algorithm to the set of users based on their characteristics. The choice of the clustering algorithm is not critical, and it depends on the type of users' characteristics. However, algorithms capable of identifying the number of clusters are preferred (e.g., [53]). It is worth noting that, the used clustering algorithm can provide more solid groups of users with similar purchasing behavior if it is applied to implicit data deriving from users' social media activity or profiles. Then, we apply discriminative itemset mining that extracts discriminant frequent patterns for each different user cluster. The use of discriminative itemset mining is an innovation in addressing the sparsity and cold-start problems. It is important to note that the proposed methodology can straightforwardly be applied also to predict the products that the user does not like. This can be achieved simply by extracting frequent itemsets and hence discriminant itemsets based on cells with cell values equal to 0. In this case, a discriminant itemset consists of products that cannot be recommended to each user of a cluster, based also on Equation (4). Then, the proposed methodology can update the initial user-product input matrix by adding zero to certain cells whether they were initially empty or not. Additionally, it is very important to note that the proposed methodology can be straightforwardly applied also to the recommendation of newly released products with no purchase history (cold-start products) to users that have bought (or rated) similar products in the past. At first, the clustering procedure is applied to a product matrix of product characteristics and then the ARM algorithm and the extraction of discriminant frequent itemsets are performed to the subsets of products. Finally, the initial user-product matrix is updated by adding 1 (recommended products) or 0 (not recommended products) cell values for those products that have never or have scarcely been bought in the past. The performed empirical tests on large-scale data (MovieLens dataset) show that the proposed methodology tackles both the sparsity problem and the _in-matrix_ and _out-of-matrix prediction_ setup of the cold-start problem by recommending more new products than the known ones, while at the same time not recommending products that the users do not like. ## 6 Conclusions

In this paper, we proposed a methodology that can handle the cold-start recommendation problem.