Future of in-vehicle recommendation systems @ Bosch

Luettin, Juergen; Rothermel, Susanne; Andrew, Mark

doi:10.1145/3298689.3346958

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…For the third set of experiments, we used a simple LSTM network that receives a sequence of visited POIs as input and outputs a prediction of the next POI in the sequence. The com-bined approach was built using HypE 5 . Our implementation and experimental settings can be found in the repository 6 on GitHub.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…Recommender systems based on location based social networks (LBSN) have been the subject of intensive recent research activities, see [3,4] for recent surveys. In-vehicle recommender systems provide even more context information such as vehicle sensor based information about occupants and driver, vehicle state, or surrounding traffic [5]. An early approach for POI recommendation based on models for human mobility and their dynamics in social networks is described in [6].…”

Section: Recommender Systems For Location Based Social Networkmentioning

confidence: 99%

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Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks

Werner¹,

Rettinger²,

Halilaj³

et al. 2021

Studies on the Semantic Web

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Learned latent vector representations are key to the success of many recommender systems in recent years. However, traditional approaches like matrix factorization produce vector representations that capture global distributions of a static recommendation scenario only. Such latent user or item representations do not capture background knowledge and are not customized to a concrete situational context and the sequential history of events leading up to it. This is a fundamentally limiting restriction for many tasks and applications, since the latent state can depend on a) abstract background information, b) the current situational context and c) the history of related observations. An illustrating example is a restaurant recommendation scenario, where a user’s assessment of the situation depends a) on taxonomical information regarding the type of cuisine, b) on situational factors like time of day, weather or location and c) on the subjective individual history and experience of this user in preceding situations. This situation-specific internal state of the user is not captured when using a traditional collaborative filtering approach, since background knowledge, the situational context and the sequential nature of an individual’s history cannot easily be represented in the matrix. In this paper, we investigate how well state-of-the-art approaches do exploit those different dimensions relevant to POI recommendation tasks. Naturally, we represent such a scenario as a temporal knowledge graph and compare plain knowledge graph, a taxonomy and a hypergraph embedding approach, as well as a recurrent neural network architecture to exploit the different context-dimensions of such rich information. Our empirical evidence indicates that the situational context is most crucial to the prediction performance, while the taxonomical and sequential information are harder to exploit. However, they still have their specific merits depending on the situation.

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Section: Experimental Setup and Resultsmentioning

confidence: 99%

Section: Recommender Systems For Location Based Social Networkmentioning

confidence: 99%

Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks

Werner¹,

Rettinger²,

Halilaj³

et al. 2021

Studies on the Semantic Web

View full text Add to dashboard Cite

show abstract

“…Here, the ultimate goal is to build a CRS running on a service robot, in this case one that is able to elicit a customer's food preferences in a restaurant. Yet another application scenario, that of future in-car recommender systems, is sketched in [81]. Given the specific situation in a driving scenario, the use of speech technology often is advisable [22], which almost naturally leads to conversational recommendation approaches, e.g., for driving-related aspects like navigation or entertainment [8,9].…”

Section: Application Environmentmentioning

confidence: 99%

A Survey on Conversational Recommender Systems

Jannach,

Manzoor,

Cai

et al. 2020

Preprint

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Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future. CCS Concepts: • Information systems → Recommender systems; • General and reference → Surveys and overviews; • Human-centered computing → Interactive systems and tools;

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“…However, currently growing concepts in the Internet domain, such as Internet of Things, autonomous driving, and augmented reality, among many others, are pushing to consider new applications of the RS. For example, we can find novel and advanced applications of RS in vehicles [19], voice-enabled devices [20], smartphones [21], and multimedia data for robustness [22], diversification [23], and real-time [24] recommendation aims, among many other examples.…”

Section: Related Workmentioning

confidence: 99%

Performance of Two Approaches of Embedded Recommender Systems

2020

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Nowadays, highly portable and low-energy computing environments require programming applications able to satisfy computing time and energy constraints. Furthermore, collaborative filtering based recommender systems are intelligent systems that use large databases and perform extensive matrix arithmetic calculations. In this research, we present an optimized algorithm and a parallel hardware implementation as good approach for running embedded collaborative filtering applications. To this end, we have considered high-level synthesis programming for reconfigurable hardware technology. The design was tested under environments where usual parameters and real-world datasets were applied, and compared to usual microprocessors running similar implementations. The performance results obtained by the different implementations were analyzed in computing time and energy consumption terms. The main conclusion is that the optimized algorithm is competitive in embedded applications when considering large datasets and parallel implementations based on reconfigurable hardware.

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Future of in-vehicle recommendation systems @ Bosch

Cited by 10 publications

References 3 publications

Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks

Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks

A Survey on Conversational Recommender Systems

Performance of Two Approaches of Embedded Recommender Systems

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