Wyze Rule Recommendation Dataset

  Dataset Summary

The Wyze Rule dataset is a new large-scale dataset designed specifically for smart home rule recommendation research. It contains over 1 million rules generated by 300,000 users from Wyze Labs, offering an extensive collection of real-world automation rules tailored to users' unique smart home setups. The goal of the Wyze Rule dataset is to advance research and development of personalized rule recommendation… See the full description on the dataset page: https://huggingface.co/datasets/wyzelabs/RuleRecommendation.

We introduce RARD II: The 2nd Related-Article Recommendation Dataset from the recommendation-as-a-service provider Mr. DLib (http://mr-dlib.org). The RARD II dataset encompasses 94m recommendations, covering an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. Information includes details on which recommendation approaches were used (e.g. content-based filtering, stereotype, most popular), what types of features were used in content based filtering (simple terms vs. keyphrases), where the features were extracted from (title or abstract), and the time when recommendations were delivered and clicked. In addition, the dataset contains an implicit item-item rating matrix that was created based on the recommendation click logs. Compared to its predecessor RARD I, RARD II contains 64% more recommendations, 187% more features (algorithms, parameters, and statistics), 50% more clicks, 140% more documents, and in addition to Sowiport, adds another service partner (i.e. JabRef). RARD II enables researchers to train machine learning algorithms for research-paper recommendations, perform offline evaluations, and do research on data from Mr. DLib’s recommender system, without implementing a recommender system themselves. RARD II is a unique dataset with high value to recommender-systems researchers, particularly in the domain of digital libraries.

Introduction

According to latest Report by Market.us, The AI-Based Recommendation System Market was valued at USD 2.8 billion in 2023 and is forecasted to expand to USD 34.4 billion by 2033, achieving a Compound Annual Growth Rate (CAGR) of 28.5% over the forecast period. This substantial growth is indicative of the increasing reliance on AI-driven solutions to enhance user experience and engagement across various digital platforms. In 2023, North America held 35.6% of the market, driven by significant advancements in AI technology.

An AI-based recommendation system uses artificial intelligence to analyze data and predict what users might prefer or need. These systems gather information from users' activities and preferences, like watching movies on a streaming platform or shopping online. Then, using algorithms, they suggest products or content that match the user’s tastes and past behavior. This makes finding new favorites easier and enhances the user experience.

The market for AI-based recommendation systems is growing as more businesses seek to personalize customer experiences. Companies across various sectors such as e-commerce, entertainment, and social media utilize these systems to increase user engagement and sales. The rise in digital data consumption and advancements in AI technologies fuel the expansion of this market. As businesses invest more in these systems to stay competitive, the market is expected to continue growing significantly in the future.

The demand for AI-based recommendation systems is escalating across multiple industries. As consumers and businesses increasingly rely on digital platforms for shopping, entertainment, and information, there's a significant need for systems that can deliver personalized experiences. E-commerce giants, streaming services, and content providers are investing heavily in these technologies to improve customer satisfaction and retention. The increasing volume of data generated online also presents a vast opportunity for these systems to leverage advanced analytics and machine learning to offer precise recommendations.

The market for AI-based recommendation systems presents numerous opportunities for growth and innovation. One major opportunity lies in integrating these systems into emerging technologies such as virtual reality and the Internet of Things (IoT), expanding their applicability beyond traditional platforms. Additionally, as machine learning models become more sophisticated, there's a chance to enhance recommendation accuracy and user trust, which can open up new business avenues. Moreover, expanding global internet access is allowing more companies to deploy sophisticated recommendation systems, potentially entering untapped markets with tailored marketing strategies.

Dataset

This dataset was created by Ritik Kumar

Released under Apache 2.0

Contents

Dataset

This dataset was created by MUDDAPU AKASH

Released under Apache 2.0

Contents

This data set deals with Maintenance Action Recommendations

Associative Tag Recommendation Exploiting Multiple Textual FeaturesFabiano Belem, Eder Martins, Jussara M. Almeida Marcos Goncalves In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, July. 2011AbstractThis work addresses the task of recommending relevant tags to a target object by jointly exploiting three dimen- sions of the problem: (i) term co-occurrence with tags preassigned to the target object, (ii) terms extracted from mul- tiple textual features, and (iii) several metrics of tag relevance. In particular, we propose several new heuristic meth- ods, which extend previous, highly effective and efficient, state-of-the-art strategies by including new metrics that try to capture how accurately a candidate term describes the object’s content. We also exploit two learning to rank techniques, namely RankSVM and Genetic Programming, for the task of generating ranking functions that combine multiple metrics to accurately estimate the relevance of a tag to a given object. We evaluate all proposed methods in various scenarios for three popular Web 2.0 applications, namely, LastFM, YouTube and YahooVideo. We found that our new heuristics greatly outperform the methods on which they are based, producing gains in precision of up to 181%, as well as another state-of-the-art technique, with improvements in precision of up to 40% over the best baseline in any scenario. Some further improvements can also be achieved, in some scenarios, with the new learning-to-rank based strategies, which have the additional advantage of being quite flexible and easily extensible to exploit other aspects of the tag recommendation problem.Bibtex Citation@inproceedings{belem@sigir11, author = {Fabiano Bel\'em and Eder Martins and Jussara Almeida and Marcos Gon\c{c}alves}, title = {Associative Tag Recommendation Exploiting Multiple Textual Features}, booktitle = {{Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR'11)}}, month = {{July}}, year = {2011} }

STS dataset was collected by a context-aware recommender system mobile app named as "South Tyrol Suggests". The app provides context-aware recommendations for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users.

Contextual variables includes

• distance: far away, near by
• time available: half day, one day, more than one day
• temperature: burning, hot, warm, cool, cold, freezing
• crowdedness: crowded, not crowded, empty
• knowledge of surroundings: new to area, returning visitor, citizen of the area
• season: spring, summer, autumn, winter
• budget: budget traveler, price for quality, high spender
• daytime: morning, noon, afternoon, evening, night
• weather: clear sky, sunny, cloudy, rainy, thunderstorm, snowing
• companion: alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children
• mood: happy, sad, active, lazy weekday: weekday, weekend
• travel goal: visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport
• means of transport: no transportation means, a bicycle, a car, public transport

More details can be found here:

Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. "Techniques for cold-starting context-aware mobile recommender systems for tourism." Intelligenza Artificiale 8, no. 2 (2014): 129-143.

The importance of online recommender systems for drugs, medical professionals, and hospitals is growing. Today, the majority of people use online consultations for drug recommendations for all types of health issues. Emergencies such as pandemics, floods, or cyclones can be helped by the medical recommender system. In the era of machine learning (ML), recommender systems produce more accurate, quick, and reliable clinical predictions with minimal costs. As a result, these systems maintain better performance, integrity, and privacy of patient data in the decision-making process and provide precise information at any time. Therefore, we present drug recommender systems with a stacked artificial neural network (ANN) model to improve the fairness and safety of treatment for infectious diseases. To reduce side effects, drugs are recommended based on a patient’s previous health profile, lifestyle, and habits. The proposed system produced results with 97.5% accuracy. A system such as this could be useful in recommending safe medicines to patients, especially during health emergencies.

Dataset

This dataset was created by Breejesh Dhar

Contents

This repository contains auxiliary materials for the iConference 2024 paper "“If I like BLANK, what else will I like?”: Analyzing
a Human Recommendation Community on Reddit" by Thi Binh Minh Cao and Toine Bogers (= corresponding author)

Published in: Proceedings of the 2024 iConference, April 15--26, 2024, Changchun, China.

The paper presents the results of an analysis of /r/ifyoulikeblank, a Reddit community dedicated to requesting and providing for recommendations. This repository contains the following auxiliary materials:

• The annotated sample of threads from the /r/ifyoulikeblank subreddit (annotated-dataset.xlsx). The second sheet in the Excel file explains the contents of the file.
• The R code for performing the analysis described in the paper (annotation-analysis.R)
• CSV file containing the genres attributed to the artists as crawled from the Spotify API (artist-spotify-genres.csv)
• CSV file containing the popularity scores crawled from the Spotify API for the seed items and Spotify recommendations (recommendation-popularity.reddit-vs-spotify.csv)
• CSV file containing the popularity scores crawled from the Spotify API for the Reddit (recommendation-popularity.reddit.csv)
• The stopwords file used in the textual analysis (stopwords.csv)
• Excel file containing the activity data for the /r/ifyoulikeblank subreddit (subreddit-stats.xlsx)

Movielens is a movie recommendation dataset widely used for benchmarking process. 385There are nearly 100,000 hard ratings on 19 different types of movies (Action, Comedy 386and so on).

The recommendation length is set to 20.

This dataset contains ratings on news about the Brazilian 2018 presidential elections. This news was recommended by different recommender algorithms including diversification strategies. So, we registered the algorithms responsible for each recommendation and this information can be used by other researchers who are focusing on diversification of recommendations, especially in the news domain. Eeah news item contains the raw text that can be used for text mining techniques in order to discover features for diversification.

During a March 2024 survey among adults in the United States, around 89 percent of respondents selected friends and family as a trustworthy product recommendation source. Recommendations from an expert reviewer followed with a share of 74 percent, whereas artificial intelligence (AI) applications such as ChatGPT and Bard ranked fifth, chosen by less than 40 percent of the interviewees.

The Content Recommendation Engine Market offers a range of products, including:Cloud-based Recommendation Engines: These engines are hosted on cloud platforms, providing scalability, flexibility, and cost-effectiveness.On-premise Recommendation Engines: These engines are installed on the user's own infrastructure, offering greater control and customization.Hybrid Recommendation Engines: These engines combine the benefits of both cloud-based and on-premise solutions. Recent developments include:

March 2021: A key player in the enterprise business process intelligence and process management arena, Signavio, was acquired by SAP SE. The products from Signavio are incorporated into SAP's business process intelligence portfolio and work in conjunction with SAP's comprehensive process transformation portfolio.

February 2021: UNBXD Inc. and Google Cloud worked together to provide retail establishments with AI-powered commerce search on Google Cloud. Unbxd intended to use Google Cloud's cutting-edge search, recommendation, and AI capabilities as part of the partnership to enhance product discovery for retail consumers. Also, the business intended to offer its Google Cloud-hosted commerce search service to retail clients.

.

Summary This dataset (ml-25m) describes a 5-star rating and free-text tagging activity from MovieLens, a movie recommendation service. It contains 25000095 ratings and 1093360 tag applications across 62423 movies. These data were created by 162541 users between January 09, 1995, and November 21, 2019. This dataset was generated on November 21, 2019. Users were selected at random for inclusion. All selected users had rated at least 20 movies. No demographic information is included. Each user is represented by an id, and no other information is provided. The data are contained in the files genome-scores.csv, genome-tags.csv, links.csv, movies.csv, ratings.csv, and tags.csv. More details about the contents and use of all these files follow. This and other GroupLens data sets are publicly available for download at

This statistic shows data on the share of consumers who are satisfied with personalized content recommendations from selected sources worldwide as of October 2017. During the survey, 78 percent of respondents stated that they were satisfied with personalized content recommendations from their streaming video services.

Music Recommendation Dataset (KGRec-music). Number of items: 8,640. Number of users: 5,199. Number of items-users interactions: 751,531. All the data comes from songfacts.com and last.fm websites. Items are songs, which are described in terms of textual description extracted from songfacts.com, and tags from last.fm. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. Multiword tags are separated by -. The name of the file is the id of the item plus the ".txt" extension. Not all items have tags, there are 401 items without tags. implicit_lf_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: user_id /t sound_id /t 1 /n. Sound Recommendation Dataset (KGRec-sound). Number of items: 21,552. Number of users: 20,000. Number of items-users interactions: 2,117,698. All the data comes from Freesound.org. Items are sounds, which are described in terms of textual description and tags created by the sound creator at uploading time. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. The name of the file is the id of the item plus the ".txt" extension. downloads_fs_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: /nuser_id /t sound_id /t 1 /n. Two different datasets with users, items, implicit feedback interactions between users and items, item tags, and item text descriptions are provided, one for Music Recommendation (KGRec-music), and other for Sound Recommendation (KGRec-sound).

This is a common Zenodo repository for both lastfm-360K and lastfm-1K datasets. See below the details of both datasets, including license, acknowledgements, contact, and instructions to cite.

LASTFM-360K (version 1.2, March 2010).

What is this? This dataset contains

Files:

usersha1-artmbid-artname-plays.tsv (MD5: be672526eb7c69495c27ad27803148f1)

usersha1-profile.tsv (MD5: 51159d4edf6a92cb96f87768aa2be678)

mbox_sha1sum.py (MD5: feb3485eace85f3ba62e324839e6ab39)

Data Statistics:

File usersha1-artmbid-artname-plays.tsv:

Total Lines: 17,559,530

Unique Users: 359,347

Artists with MBID: 186,642

Artists without MBID: 107,373

Data Format: The data is formatted one entry per line as follows (tab separated "\t"):

File usersha1-artmbid-artname-plays.tsv:

user-mboxsha1 \t musicbrainz-artist-id \t artist-name \t plays

File usersha1-profile.tsv:

user-mboxsha1 \t gender (m|f|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)

Example:

File usersha1-artmbid-artname-plays.tsv:

000063d3fe1cf2ba248b9e3c3f0334845a27a6be \t a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432 \t u2 \t 31 ...

File usersha1-profile.tsv:

000063d3fe1cf2ba248b9e3c3f0334845a27a6be \t m \t 19 \t Mexico \t Apr 28, 2008 ...

LASTFM-1K (version 1.0, March 2010).

What is this? This dataset contains

Files:

userid-timestamp-artid-artname-traid-traname.tsv (MD5: 64747b21563e3d2aa95751e0ddc46b68)

userid-profile.tsv (MD5: c53608b6b445db201098c1489ea497df)

Data Statistics:

File userid-timestamp-artid-artname-traid-traname.tsv:

Total Lines: 19,150,868

Unique Users: 992

Artists with MBID: 107,528

Artists without MBDID: 69,420

Data Format: The data is formatted one entry per line as follows (tab separated, "\t"):

File userid-timestamp-artid-artname-traid-traname.tsv:

userid \t timestamp \t musicbrainz-artist-id \t artist-name \t musicbrainz-track-id \t track-name

File userid-profile.tsv:

userid \t gender ('m'|'f'|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)

Example:

File userid-timestamp-artid-artname-traid-traname.tsv:

user_000639 \t 2009-04-08T01:57:47Z \t MBID \t The Dogs D'Amour \t MBID \t Fall in Love Again? user_000639 \t 2009-04-08T01:53:56Z \t MBID \t The Dogs D'Amour \t MBID \t Wait Until I'm Dead ...

File userid-profile.tsv:

user_000639 \t m \t Mexico \t Apr 27, 2005 ...

LICENSE OF BOTH DATASETS. The data contained in both datasets is distributed with permission of Last.fm. The data is made available for non-commercial use. Those interested in using the data or web services in a commercial context should contact:

partners [at] last [dot] fm

For more information see Last.fm terms of service

ACKNOWLEDGEMENTS. Thanks to Last.fm for providing the access to this data via their web services. Special thanks to Norman Casagrande.

REFERENCES. When using this dataset you must reference the Last.fm webpage. Optionally (not mandatory at all!), you can cite Chapter 3 of this book:

@book{Celma:Springer2010, author = {Celma, O.}, title = {{Music Recommendation and Discovery in the Long Tail}}, publisher = {Springer}, year = {2010} }

CONTACT: This data was collected by Òscar Celma @ MTG/UPF