34,615 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
Large Language Models (LLMs) excel at tackling various natural language
tasks. However, due to the significant costs involved in re-training or
fine-tuning them, they remain largely static and difficult to personalize.
Nevertheless, a variety of applications could benefit from generations that are
tailored to users' preferences, goals, and knowledge. Among them is web search,
where knowing what a user is trying to accomplish, what they care about, and
what they know can lead to improved search experiences. In this work, we
propose a novel and general approach that augments an LLM with relevant context
from users' interaction histories with a search engine in order to personalize
its outputs. Specifically, we construct an entity-centric knowledge store for
each user based on their search and browsing activities on the web, which is
then leveraged to provide contextually relevant LLM prompt augmentations. This
knowledge store is light-weight, since it only produces user-specific aggregate
projections of interests and knowledge onto public knowledge graphs, and
leverages existing search log infrastructure, thereby mitigating the privacy,
compliance, and scalability concerns associated with building deep user
profiles for personalization. We then validate our approach on the task of
contextual query suggestion, which requires understanding not only the user's
current search context but also what they historically know and care about.
Through a number of experiments based on human evaluation, we show that our
approach is significantly better than several other LLM-powered baselines,
generating query suggestions that are contextually more relevant, personalized,
and useful
Personalized Ranking in eCommerce Search
We address the problem of personalization in the context of eCommerce search.
Specifically, we develop personalization ranking features that use in-session
context to augment a generic ranker optimized for conversion and relevance. We
use a combination of latent features learned from item co-clicks in historic
sessions and content-based features that use item title and price.
Personalization in search has been discussed extensively in the existing
literature. The novelty of our work is combining and comparing content-based
and content-agnostic features and showing that they complement each other to
result in a significant improvement of the ranker. Moreover, our technique does
not require an explicit re-ranking step, does not rely on learning user
profiles from long term search behavior, and does not involve complex modeling
of query-item-user features. Our approach captures item co-click propensity
using lightweight item embeddings. We experimentally show that our technique
significantly outperforms a generic ranker in terms of Mean Reciprocal Rank
(MRR). We also provide anecdotal evidence for the semantic similarity captured
by the item embeddings on the eBay search engine.Comment: Under Revie
Towards personalization in digital libraries through ontologies
In this paper we describe a browsing and searching personalization system for digital libraries based on the use of ontologies for describing the relationships between all the
elements which take part in a digital library scenario of use. The main goal of this project is to help the users of a digital library to improve their experience of use by means of two complementary strategies: first, by maintaining a complete history record of his or her browsing and searching activities, which is part of a navigational user profile which includes preferences and all the aspects related to community involvement; and second, by reusing all the knowledge which has been extracted from previous usage from other users with similar profiles. This can be accomplished in terms of narrowing and focusing the search results and browsing options through the use of a recommendation system which organizes such results in the most appropriate manner, using ontologies and concepts drawn from the semantic web field. The complete integration of the experience of use of a digital library in the learning process is also pursued. Both the usage and information organization can be also exploited to extract useful knowledge from the way users interact with a digital library, knowledge that can be used to improve several design aspects of the library, ranging from internal organization aspects to human factors and user interfaces. Although this project is still on an early development stage, it is possible to identify all the desired functionalities and requirements that are necessary to fully integrate the use of a digital library in an e-learning environment
Domain-specific queries and Web search personalization: some investigations
Major search engines deploy personalized Web results to enhance users'
experience, by showing them data supposed to be relevant to their interests.
Even if this process may bring benefits to users while browsing, it also raises
concerns on the selection of the search results. In particular, users may be
unknowingly trapped by search engines in protective information bubbles, called
"filter bubbles", which can have the undesired effect of separating users from
information that does not fit their preferences. This paper moves from early
results on quantification of personalization over Google search query results.
Inspired by previous works, we have carried out some experiments consisting of
search queries performed by a battery of Google accounts with differently
prepared profiles. Matching query results, we quantify the level of
personalization, according to topics of the queries and the profile of the
accounts. This work reports initial results and it is a first step a for more
extensive investigation to measure Web search personalization.Comment: In Proceedings WWV 2015, arXiv:1508.0338
Automated user modeling for personalized digital libraries
Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to
improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in
an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy userâs necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information
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