Search results for "Recommender system"

At Your Service: Coffee Beans Recommendation From a Robot Assistant

2020

With advances in the field of machine learning, precisely algorithms for recommendation systems, robot assistants are envisioned to become more present in the hospitality industry. Additionally, the COVID-19 pandemic has also highlighted the need to have more service robots in our everyday lives, to minimise the risk of human to-human transmission. One such example would be coffee shops, which have become intrinsic to our everyday lives. However, serving an excellent cup of coffee is not a trivial feat as a coffee blend typically comprises rich aromas, indulgent and unique flavours and a lingering aftertaste. Our work addresses this by proposing a computational model which recommends optima…

CitySearcher: A City Search Engine For Interests

2017

We introduce CitySearcher, a vertical search engine that searches for cities when queried for an interest. Generally in search engines, utilization of semantics between words is favorable for performance improvement. Even though ambiguous query words have multiple semantic meanings, search engines can return diversified results to satisfy different users' information needs. But for CitySearcher, mismatched semantic relationships can lead to extremely unsatisfactory results. For example, the city Sale would incorrectly rank high for the interest shopping because of semantic interpretations of the words. Thus in our system, the main challenge is to eliminate the mismatched semantic relationsh…

The crowd against the few: Measuring the impact of expert recommendations

2020

Abstract A large amount of research on recommender systems has focused on improving the accuracy of suggestions in offline settings. However, this focus and the commonly used techniques can lead to a “filter bubble”, severely limiting the diversity of content discovered by users. Several offline studies show that this can be mitigated by using experts for recommendation. In contrast to standard recommender systems, experts are able to generate more diverse recommendations and increase the novelty of given suggestions. They can be used in missing-data or cold-start scenarios and reduce noise in the users' ratings. This paper examines the impact of employed experts' recommendations on user be…

Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

2017

Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. Ac…

2020

Recommender systems are information software that retrieves relevant items for users from massive sources of data. The variational autoencoder (VAE) has proven to be a promising approach for recommendation systems, as it can explore high-level user-item relations and extract contingencies from the input effectively. However, the previous variants of VAE have so far seen limited application to domain-specific recommendations that require additional side information. Hence, The Ensemble Variational Autoencoder framework for recommendations (EnsVAE) is proposed. This architecture specifies a procedure to transform sub-recommenders’ predicted utility matrix into interest probabilities that allo…

An approach based on the Adaptive Resonance Theory for analysing the viability of recommender systems in a citizen Web portal

2007

This paper proposes a methodology to optimise the future accuracy of a collaborative recommender application in a citizen Web portal. There are four stages namely, user modelling, benchmarking of clustering algorithms, prediction analysis and recommendation. The first stage is to develop analytical models of common characteristics of Web-user data. These artificial data sets are then used to evaluate the performance of clustering algorithms, in particular benchmarking the ART2 neural network with K-means clustering. Afterwards, it is evaluated the predictive accuracy of the clusters applied to a real-world data set derived from access logs to the citizen Web portal Infoville XXI (http://www…

Kolaboratīvā filtrēšana ieteikumu sistēmās

2021

Darbs bija veltīts kolaboratīvai filtrēšanai ieteikumu sistēmās. Tika raksturota kolaboratīvās filtrēšanas metode, apskatīti galvēnie izaicinājumi, piemērām, datu nepietiekamība, mērogojamība u.c.. Sīkāk tika apskatīta uz atmiņu balstītas kolaboratīvās filtrēšanas metodes, uz modeļiem balstītas kolaboratīvās filtrēšanas metodes, hibrīdas kolaboratīvās filtrēšanas metodes un kolaboratīvās filtrēšanas novērtēšanas metrika. Praktiski tika apskatīts datu piemērs ar uz saturu balstītiem ieteikumiem un uz atmiņu balstītam kolaboratīvās filtrēšanas metodēm.

Open educational resources repositories literature review – Towards a comprehensive quality approaches framework

2015

Display Omitted A comprehensive literature review on learning object repositories (LOR) quality approaches.Most cited quality approaches are "peer reviews" and "recommendation systems".User-generated, collaborative, quality instruments are favored for their sustainability.Main result is a Quality approach framework for LOR design. Today, Open Educational Resources (OER) are commonly stored, used, adapted, remixed and shared within Learning object repositories (LORs) which have recently started expanding their design to support collaborative teaching and learning. As numbers of OER available freely keep on growing, many LORs struggle to find sustainable business models and get the users' att…

Customer recommendation based on profile matching and customized campaigns in on-line social networks

2019

We propose a general framework for the recommendation of possible customers (users) to advertisers (e.g., brands) based on the comparison between On-Line Social Network profiles. In particular, we associate suitable categories and subcategories to both user and brand profiles in the considered On-line Social Network. When categories involve posts and comments, the comparison is based on word embedding, and this allows to take into account the similarity between the topics of particular interest for a brand and the user preferences. Furthermore, user personal information, such as age, job or genre, are used for targeting specific advertising campaigns. Results on real Facebook dataset show t…

A survey of serendipity in recommender systems

2016

We summarize most efforts on serendipity in recommender systems.We compare definitions of serendipity in recommender systems.We classify the state-of-the-art serendipity-oriented recommendation algorithms.We review methods to assess serendipity in recommender systems.We provide the future directions of serendipity in recommender systems. Recommender systems use past behaviors of users to suggest items. Most tend to offer items similar to the items that a target user has indicated as interesting. As a result, users become bored with obvious suggestions that they might have already discovered. To improve user satisfaction, recommender systems should offer serendipitous suggestions: items not …