Our digital life consists of activities that are organized around tasks and exhibit different user states in the digital contexts around these activities. Previous works have shown that digital activity monitoring can be used to predict entities that users will need to perform digital tasks. There have been methods developed to automatically detect the tasks of a user. However, these studies typically support only specific applications and tasks, and relatively little research has been conducted on real-life digital activities. This article introduces user state modeling and prediction with contextual information captured as entities, recorded from real-world digital user behavior, called entity footprinting —a system that records users’ digital activities on their screens and proactively provides useful entities across application boundaries without requiring explicit query formulation. Our methodology is to detect contextual user states using latent representations of entities occurring in digital activities. Using topic models and recurrent neural networks, the model learns the latent representation of concurrent entities and their sequential relationships. We report a field study in which the digital activities of 13 people were recorded continuously for 14 days. The model learned from this data is used to (1) predict contextual user states and (2) predict relevant entities for the detected states. The results show improved user state detection accuracy and entity prediction performance compared to static, heuristic, and basic topic models. Our findings have implications for the design of proactive recommendation systems that can implicitly infer users’ contextual state by monitoring users’ digital activities and proactively recommending the right information at the right time.

Modern online platforms for user modeling and recommendation require complex data infrastructures to collect and process data. Some of this data has to be kept to later be used in batches to train personalization models. However, since user activity data can be generated at very fast rates it is also useful to have algorithms able to process data streams online, in real time. Given the continuous and potentially fast change of content, context and user preferences or intents, stream-based models, and their synchronization with batch models can be extremely challenging. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably over long periods of time. Models able to continuously learn from such flows of data are gaining attention in the recommender systems community, and are being increasingly deployed in online platforms. However, many challenges associated with learning from streams need further investigation.

Modern online systems for user modeling and recommendation need to continuously deal with complex data streams generated by users at very fast rates. This can be overwhelming for systems and algorithms designed to train recommendation models in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably for long periods of time. Online models that continuously learn from such flows of data are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online.

Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content – e.g. posts, news, products, comments –, but also user feedback – e.g. ratings, views, reads, clicks –, together with context data – user device, spacial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online.

Analogies are 4-ary relations of the form "A is to B as C is to D". When A, B and C are fixed, we call analogical equation the problem of finding the correct D. A direct applicative domain is Natural Language Processing, in which it has been shown successful on word inflections, such as conjugation or declension. If most approaches rely on the axioms of proportional analogy to solve these equations, these axioms are known to have limitations, in particular in the nature of the considered flections. In this paper, we propose an alternative approach, based on the assumption that optimal word inflections are transformations of minimal complexity. We propose a rough estimation of complexity for word analogies and an algorithm to find the optimal transformations. We illustrate our method on a large-scale benchmark dataset and compare with state-of-the-art approaches to demonstrate the interest of using complexity to solve analogies on words.

Modern online web-based systems continuously generate data at very fast rates. This continuous flow of data encompasses web content – e.g. posts, news, products, comments –, but also user feedback – e.g. ratings, views, reads, clicks, thumbs up –, as well as context information – device used, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design to train in offline batches, given the continuous and potentially fast change of content, context and user preferences. Therefore it is important to investigate online methods to be able to transparently adapt to the inherent dynamics of online systems. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online, as data is generated.

The ever-growing nature of user generated data in online systems poses obvious challenges on how we process such data. Typically, this issue is regarded as a scalability problem and has been mainly addressed with distributed algorithms able to train on massive amounts of data in short time windows. However, data is inevitably adding up at high speeds. Eventually one needs to discard or archive some of it. Moreover, the dynamic nature of data in user modeling and recommender systems, such as change of user preferences, and the continuous introduction of new users and items make it increasingly difficult to maintain up-to-date, accurate recommendation models. The objective of this workshop is to bring together researchers and practitioners interested in incremental and adaptive approaches to stream-based user modeling, recommendation and personalization, including algorithms, evaluation issues, incremental content and context mining, privacy and transparency, temporal recommendation or software frameworks for continuous learning.

Extracting global themes from a written text has recently become a major issue for computational intelligence, in particular in Natural Language Processing communities. Among all proposed solutions, Latent Dirichlet Allocation (LDA) has gained a vast interest and several variants have been proposed to adapt to changing environments. With the emergence of data streams, for instance from social media, the domain faces a new challenge: topic extraction in real time. In this paper, we propose a simple approach called Adaptive Window based Incremental LDA (AWILDA) originating from the cross-over between LDA and state-of-the-art methods in data stream mining. We train new topic models only when a drift is detected and select training data on the fly using ADWIN algorithm. We provide both theoretical guarantees for our method and experimental validation on artificial and real-world data.

Collaborative filtering (CF) mainly suffers from rating sparsity and from the cold-start problem. Auxiliary information like texts and images has been leveraged to alleviate these problems, resulting in hybrid recommender systems (RS). Due to the abundance of data continuously generated in real-world applications, it has become essential to design online RS that are able to handle user feedback and the availability of new items in real-time. These systems are also required to adapt to drifts when a change in the data distribution is detected. In this paper, we propose an adaptive collaborative topic modeling approach, CoAWILDA, as a hybrid system relying on adaptive online Latent Dirichlet Allocation (AWILDA) to model newly available items arriving as a document stream and incremental matrix factorization for CF. The topic model is maintained up-to-date in an online fashion and is retrained in batch when a drift is detected using documents automatically selected by an adaptive windowing technique. Our experiments on real-world datasets prove the effectiveness of our approach for online recommendation.