These datasets include ratings as well as social (or trust) relationships between users. Data are from LibraryThing (a book review website) and epinions (general consumer reviews).

Metadata includes

• reviews

• price paid (epinions)

• helpfulness votes (librarything)

• flags (librarything)

eLearning, or online learning, has reached every corner of the globe in this era of digitization. As a result of the COVID-19 pandemic, the value of eLearning has increased substantially. In eLearning recommendation systems, information overload, personalised suggestion, sparsity, and accuracy are all major problems. The correct eLearning Recommendation System is necessary to tailor the course recommendation according to the user's needs. To create this model, dataset of the User Profile and User Rating is needed. The User Profile dataset is created by using the Calyxpod programme to collect student profiles. User requirements are available through these profiles. The dataset obtained by gathering student comments following course completion is in the range of 1 (lowest) to 5 (highest).

These datasets contain attributes about products sold on ModCloth and Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed). Data also includes user/item interactions for recommendation.

Metadata includes

• ratings

• product images

• user identities

• item sizes, user genders

This dataset contains survey results collected from new recommendation system. This dataset asks about how the people accept recommendation systems from the AI trustworthiness and recommendation quality aspect.

Dataset

This dataset was created by Raihan Sikdar

Contents

These datasets contain reviews from the Goodreads book review website, and a variety of attributes describing the items. Critically, these datasets have multiple levels of user interaction, raging from adding to a shelf, rating, and reading.

Metadata includes

• reviews

• add-to-shelf, read, review actions

• book attributes: title, isbn

• graph of similar books

Basic Statistics:

• Items: 1,561,465

• Users: 808,749

• Interactions: 225,394,930

Market Overview:

The global recommendation engine market size reached US$ 4.8 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 59.1 Billion by 2032, exhibiting a growth rate (CAGR) of 31.2% during 2024-2032.

Recommendation engine refers to a data filtering tool that enables marketers to offer relevant product recommendations to customers in real-time. It is leveraged with data analysis techniques and advanced algorithms, such as machine learning (ML) and artificial intelligence (AI), which can suggest relevant product catalogs to an individual. In addition, it can show products on websites, apps, and emails, based on customer preferences, past browser history, attributes, and situational context. At present, it is widely utilized in business-to-consumer (B2C) e-commerce fields, such as entertainment, mobile apps, and education, which require a personalization strategy.

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Recommendation Engine Market Trends:

The coronavirus disease (COVID-19) pandemic and complete lockdowns imposed by governing agencies of numerous countries have encouraged enterprises to shift to online retail platforms. This represents one of the major factors catalyzing the demand for recommendation engines to increase sales and maintain a positive customer relationship. Apart from this, the thriving e-commerce industry on account of the increasing penetration of the Internet, the growing reliance on smartphones, and the emerging social media trend are contributing to the market growth. This can also be attributed to changing consumer spending habits and the rising need for convenience, immediacy, and simplicity during shopping. Moreover, the increasing adoption of the omnichannel approach to sales that focuses on providing a seamless customer experience is driving the market. Furthermore, due to the rapid expansion of businesses globally, there is a rise in the demand for recommendation engines to manage large volumes of data and engage users actively. They are also gaining traction in small and medium-sized enterprises (SMEs) worldwide to enable them to increase overall sales by cross-selling new products to existing customers and maximize average order value.

Key Market Segmentation:

IMARC Group provides an analysis of the key trends in each sub-segment of the global recommendation engine market report, along with forecasts at the global, regional and country level from 2024-2032. Our report has categorized the market based on type, technology, deployment mode, application and end user.

Breakup by Type:

• Collaborative Filtering
• Content-based Filtering
• Hybrid Recommendation Systems
• Others
  

Breakup by Technology:

• Context Aware
• Geospatial Aware
  

Breakup by Deployment Mode:

• On-premises
• Cloud-based
  

Breakup by Application:

• Strategy and Operations Planning
• Product Planning and Proactive Asset Management
• Personalized Campaigns and Customer Discovery
  

Breakup by End User:

• IT and Telecommunication
• BFSI
• Retail
• Media and Entertainment
• Healthcare
• Others
  

Breakup by Region:

• North America
  • United States
  • Canada
• Asia-Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
• Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Russia
  • Others
• Latin America
  • Brazil
  • Mexico
  • Others
• Middle East and Africa
  

Competitive Landscape:

The competitive landscape of the industry has also been examined along with the profiles of the key players being Adobe Inc., Amazon.com Inc., Dynamic Yield (McDonald's), Google LLC (Alphabet Inc.), Hewlett Packard Enterprise Development LP, Intel Corporation, International Business Machines Corporation, Kibo Software Inc., Microsoft Corporation,

The CDEI has been tasked with researching the ways in which algorithmically driven recommendation systems have impacted music consumption, including how creators are being affected (see Recommendation 18 in the government’s response to the economics of music streaming Committee’s Second Report). The CDEI will be carrying out a survey to take the views of creators into consideration as part of our research, as well as begin to understand if and how algorithmically driven recommendation systems affect different categories of creators, creators across different genres, and whether there are any apparent differences in their effect by region, age, gender identity, or ethnic group. This privacy notice explains who the CDEI are, the personal data the CDEI collects, how the CDEI uses it, who the CDEI shares it with, and what your legal rights are.

Context

During the last few decagons, with the rise of Youtube, Amazon, Netflix and many other such web business, recommender systems had take view and more place in our lives. After e-commerce (suggest to purchasing product that could interest them) on online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys. In a very general way, recommender systems am designs aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy or anything els depending on industries).

Recommender system are really critical in some industries as they bottle generate one huge amount of revenues when they are efficient or other be ampere way to stand out significantly from competitor. As a proving of the importance of recommender systems, we can mention that, a few years ago, Netflix organised a challenges (the “Netflix prize”) where the goal has to production a recommender regelung that performs beats than its own algorism with one prize of 1 million dollars until win.

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Content

The Book-Crossing dataset comprises 3 files. - Users Contains which users. Note that user IDs (User-ID) have been anonymized also cards to integers. Demographic data is provided (Location, Age) if available. Otherwise, these fields contain NULL-values. #
- Books Books can identified by their respective ISBN. Invalid ISBNs have formerly being removed from the dataset. Moreover, some content-based information is given (Book-Title, Book-Author, Year-Of-Publication, Publisher), obtained from Amazon Web Services. Note this in case from several authors, only the first are given. URLs linking to screen gallery are also defined, appearing in three different flavorings (Image-URL-S, Image-URL-M, Image-URL-L), i.e., small, center, large. These URLs point till the Amazon web site. #
- Ratings Contains the book rating information. Reviews (Book-Rating) are either plain, expressed on a ascend from 1-10 (higher values indicate higher appreciation), oder implicit, expressed by 0. #

Starter Kernel(s)

• Recom I: Data Understanding press Simple Recommendation

Acknowledgements

Collected by Cai-Nicolas Ziegler in a 4-week crawl (August / September 2004) from the Book-Crossing community with kind permission from Ron Hornbaker, CTO concerning Humankind Systems. Contains 278,858 users (anonymized but with demographic information) providing 1,149,780 ratings (explicit / implicit) about 271,379 books.

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More Readings

• Deep Learning based Recommender Structure: A Survey and New Perspectives

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.

According to a survey conducted among consumers in the United States in August 2022, 71 percent of respondents said they had purchased products or services based on recommendations from family, while 66 percent said they did so based on suggestions from close friends. Gen X respondents were those most inclined towards following the recommendations of family and friends. In contrast, Gen Z and Millennial respondents were more likely to adhere to the endorsements of companies or brands on social media.

Movie recommendation task based on the Movielens dataset

Dataset

This dataset was created by Noor Saeed

Contents

Dataset

This dataset was created by Cyber Cop

Released under GNU Affero General Public License 3.0

Contents

ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users recommend movies to each other. The dataset consists of over 10,000 conversations centered around the theme of providing movie recommendations.

These datasets contain social connections and circles from Facebook, Twitter, and Google Plus.

Metadata includes

• social connections

• circles (sets of friends sharing a common property)

• user metadata

apply TODIM to recommend trading model for industry

This is a mutli-modal dataset for restaurants from Google Local (Google Maps). Data includes images and reviews posted by users, as well as metadata for each restaurant.

Myket Android Application Install Dataset

This dataset contains information on application install interactions of users in the Myket android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions.

  Data Creation

The dataset was initially generated by the Myket data team, and later cleaned and subsampled by Erfan Loghmani a master student… See the full description on the dataset page: https://huggingface.co/datasets/erfanloghmani/myket-android-application-recommendation-dataset.

This dataset was made by augmenting optimum soil and environmental characteristics for crop growth