Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2024 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

Recommender systems (RSs) have become one of the major applications that aim to tailor items to the user’s preferences. Traditional recommendation algorithms capture users’ interests and their interactions with items without taking into account contextual information, such as time and location. However, user interests may change depending on the context [1] . In real-life applications, there is plenty of information regarding user’s circumstances and surroundings (e.g., the activity of the user, time, location, weather, etc.). Such contextual information can be high-dimensional and is gathered from multiple sources, such as web pages, mobile devices, and more. RSs taking context information into account are called context-aware recommender systems (CARSs) [1] .

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2023 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2022 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

In this paper, we propose a hierarchical representation of latent contextual information that captures contextual situations in which users are recommended particular items. We also introduce an algorithm that converts unstructured latent contextual information into structured hierarchical representations. In addition, we present two general context-aware recommendation algorithms that extend collaborative filtering (CF) approaches and utilize structured and unstructured latent contextual information. In particular, the first algorithm utilizes structured latent contexts and the second one combines the structured and the unstructured latent contextual representations. By using latent contextual information in a recommendation model, we capture and represent both the structure of the latent context in the form of a hierarchy and the values of contextual variables in the form of an unstructured vector. We tested the two proposed methods with two CF-based methods on several context-rich datasets under different experimental settings. We show that using hierarchical latent contextual representations leads to significantly better recommendations than the baselines for the datasets having high- and medium-dimensional contexts. Although this is not the case for the low-dimensional contextual data, the hybrid approach, combining structured and unstructured latent contextual information, significantly outperforms other baselines across all the experimental settings and dimensions of contextual data.

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2021 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2020 workshop provides a venue for presenting and discussing approaches for the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in online environments.

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2019 workshop provides a venue for presenting and discussing approaches for next generation of CARS and application domains that may require a variety of dimensions of contexts and cope with its dynamic properties.

Attack propagation models within honeypot systems aim at providing insights about attack strategies that target multiple honeypots, rather than analyzing attacks on each honeypot separately. Traditional attack propagation models focus on building a single probabilistic model. This modeling approach may be misleading, since it does not take into consideration contextual information such as the country from which the attack is initiated. In addition, with the massive increase in the magnitude of attacks on honeypots, a scalable modeling approach is required. In this work we present a novel attack propagation model that can utilize contextual information about the attacks by training multiple Markov Chain models. Moreover, we add additional layers of analysis: first, we present a likelihood estimation procedure that can identify new and evolving attack patterns; and second, we introduce a method for generating simulated attack sequences that can be used for training or sensitivity analysis. Lastly, we present, in details, a MapReduce design for all suggested algorithms in order to address scalability issues. We evaluate our methods on a massive dataset which includes approximately 170 million attacks on an operational honeypot system. Results indicate that contextual modeling is important for explaining attack propagation that may vary by country. In addition, we show the effectiveness of the suggested method for generating simulated sequences by comparing the attack propagation patterns we learned in the generated dataset and the original one. Finally, we demonstrate the scalability of all of the proposed algorithms on real and synthetic datasets that include over a billion records.