Showing 9-16 of 19 results
TripAdvisor European restaurants

TripAdvisor European restaurants

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2021
Creators: (Leone, Stefano)

TripAdvisor is the most popular travel website and it stores data for almost all restaurants, showing locations (even latitude and longitude coordinates), restaurant descriptions, user ratings and reviews, and many more aspects. The dataset is 0.68 GB large.

The TripAdvisor dataset includes 1,083,397 restaurants with attributes such as location data, average rating, number of reviews, open hours, cuisine types, awards, etc.

The dataset combines the restaurants from the main European countries, the data has been scraped in early May 2021.

The dataset is structured with various variables for each restaurant, such as:​

  • restaurant_link: Unique TripAdvisor restaurant link.​
  • restaurant_name: Name of the restaurant on TripAdvisor.​
  • original_location: Original location displayed on TripAdvisor.​
  • country: Country name retrieved from original_location.​
  • region: Region name retrieved from original_location.​
  • province: Province name retrieved from original_location.​
  • city: City name retrieved from original_location.​
  • address: Address displayed on TripAdvisor.​
  • latitude: Latitude coordinate.​
  • longitude: Longitude coordinate.​
  • claimed: Indicates if the restaurant business is claimed on TripAdvisor.​
  • awards: Award names.​
  • popularity_detailed: Detailed popularity ranking.​
  • popularity_generic: Generic popularity ranking (among all places to eat in the area).
Amazon product co-purchasing network metadata

Amazon product co-purchasing network metadata

Stefan
Open Dataset
Creators: Leskovec, Jure
Publication Date: 2006
Creators: Leskovec, Jure

The data was collected by crawling the Amazon website and contains product metadata and review information about 548,552 different products (Books, music CDs, DVDs and VHS video tapes). It is valuable for analyzing product relationships, customer behavior, and the dynamics of product co-purchasing networks. For each product the following information is available:

Title
Salesrank
List of similar products (that get co-purchased with the current product)
Detailed product categorization
Product reviews: time, customer, rating, number of votes, number of people that found the review helpful.

The data was collected in summer 2006. It has a size of 201 MB and structured into:

  • Product Metadata: Information such as product ID, ASIN, title, group, sales rank, similar products, and categories.​

  • Product Reviews: Details including review time, customer ID, rating, number of votes, and helpfulness votes.

Web data: Amazon movie reviews

Web data: Amazon movie reviews

Stefan
Open Dataset
Creators: McAuley, Julian; Leskovec, Jure
Publication Date: 2012
Creators: McAuley, Julian; Leskovec, Jure

This dataset is a collection of approximately 8 million movie reviews from Amazon, spanning over a decade up to October 2012. It is particularly valuable for analyzing consumer behavior, sentiment analysis, and the evolution of user expertise in online reviews. In total, the dataset has a size of 3,1 GB. Each review includes detailed information such as the product’s unique identifier (ASIN), user ID, profile name, helpfulness rating, score, time of review (in Unix time), summary, and the full text of the review.

The dataset is organized with each review capturing multiple attributes:​

  • Product Information: Including the product’s unique identifier (ASIN).​

  • User Information: Such as user ID and profile name.​

  • Review Details: Encompassing helpfulness rating, score, time of review, summary, and the full text.

 

Web data: Amazon Fine Foods reviews

Web data: Amazon Fine Foods reviews

Stefan
Open Dataset
Creators: McAuley, Julian; Leskovec, Jure
Publication Date: 2012
Creators: McAuley, Julian; Leskovec, Jure

This dataset consists of reviews of fine foods from amazon. The data is 116 MB ins size and spans a period of more than 10 years, including all ~500,000 reviews up to October 2012. Each review includes detailed information such as the product’s unique identifier (ASIN), user ID, profile name, helpfulness rating, score, time of review (in Unix time), summary, and the full text of the review. This dataset is particularly valuable for analyzing consumer behavior, sentiment analysis, and the evolution of user expertise in online reviews.

The dataset is organized with each review capturing multiple attributes:​

  • Product Information: Including the product’s unique identifier (ASIN).​

  • User Information: Such as user ID and profile name.​

  • Review Details: Encompassing helpfulness rating, score, time of review, summary, and the full text.

 

Google web graph

Google web graph

Stefan
Open Dataset
Creators: Leskovec, Jure; Lang, Kevin J.; Dasgupta, Anirban; Mahoney, Michael W.
Publication Date: 2002
Creators: Leskovec, Jure; Lang, Kevin J.; Dasgupta, Anirban; Mahoney, Michael W.

The Google Web Graph dataset offers a detailed representation of the web’s hyperlink structure as captured in 2002. In this dataset, nodes correspond to individual web pages, and directed edges represent hyperlinks from one page to another. This structure is particularly valuable for studying web connectivity, page importance algorithms like PageRank, and the overall topology of the internet during that period. In total, the dataset has a size of 0,02 GB and comprises 875,713 nodes and 5,105,039 directed edges, reflecting the extensive interlinking characteristic of the early 2000s web. Structurally, the dataset is presented as a single directed graph where each node represents a web page, and each directed edge denotes a hyperlink from one page to another. This format facilitates analyses of web page connectivity, identification of influential pages, and exploration of community structures within the web.

Amazon product co-purchasing network and ground-truth communities

Amazon product co-purchasing network and ground-truth communities

Stefan
Open Dataset
Creators: Yang, Jaewon; Leskovec, Jure
Publication Date: 2012
Creators: Yang, Jaewon; Leskovec, Jure

This dataset provides a comprehensive view of product relationships on Amazon, based on the “Customers Who Bought This Item Also Bought” feature. Products are represented as nodes, and an undirected edge between two products signifies frequent co-purchasing, reflecting consumer buying patterns and product associations. ​If a product i is frequently co-purchased with product j, the graph contains an undirected edge from i to j. Each product category provided by Amazon defines each ground-truth community. We regard each connected component in a product category as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. The dataset has a size of 0,01 GB and encompasses 334,863 nodes (products) and 925,872 edges (co-purchasing relationships).

The dataset is structured into:​

  • Network Data: An undirected graph where nodes represent products, and edges indicate co-purchasing relationships.​

  • Ground-Truth Communities: Each product category defined by Amazon serves as a ground-truth community. Connected components within these categories are treated as separate communities, excluding those with fewer than three nodes. Additionally, the dataset provides the top 5,000 communities with the highest quality, as detailed in the associated research paper.

AmazonQA

AmazonQA

Stefan
Open Dataset
Creators: Gupta, Mansi; Kulkarni, Nitish; Chanda, Raghuveer; Rayasam, Anirudha; Lipton, Zachary C.
Publication Date: 2019
Creators: Gupta, Mansi; Kulkarni, Nitish; Chanda, Raghuveer; Rayasam, Anirudha; Lipton, Zachary C.

We introduce a new dataset and propose a method that combines information retrieval techniques for selecting relevant reviews (given a question) and “reading comprehension” models for synthesizing an answer (given a question and review). Our dataset consists of 923k questions, 3.6M answers and 14M reviews across 156k products. Building on the well-known Amazon dataset, we collect additional annotations, marking each question as either answerable or unanswerable based on the available reviews. This dataset is particularly valuable for developing models that integrate information retrieval techniques to select relevant reviews and “reading comprehension” models to synthesize answers based on those reviews. The dataset is approximately 4 GB in size and is available in JSON format.

The dataset uses the following variables:​

  • questionText: The text of the question posed by the consumer.​

  • questionType: Indicates whether the question is ‘yes/no’ for boolean questions or ‘descriptive’ for open-ended questions.​

  • review_snippets: A list of extracted review snippets relevant to the question (up to ten).​

  • answerText: The text of the answer provided.​

  • answerType: Specifies the type of answer.​

  • helpful: A list containing two integers; the first indicates the number of users who found the answer helpful, and the second indicates the total number of responses.​

  • asin: The unique Amazon Standard Identification Number (ASIN) for the product the question pertains to.​

  • qid: A unique question identifier within the dataset.​

  • category: The product category.​

  • top_review_wilson: The review with the highest Wilson score.​

  • top_review_helpful: The review voted as most helpful by users.​

  • is_answerable: A boolean indicating whether the question is answerable using the review snippets, based on an answerability classifier.​

  • top_sentences_IR: A list of top sentences (up to ten) based on Information Retrieval (IR) score with the question.

Amazon question/answer data

Amazon question/answer data

Stefan
Open Dataset
Creators: McAuley, Julian; Yang, Alex
Publication Date: 2016
Creators: McAuley, Julian; Yang, Alex
This dataset contains Question and Answer data from Amazon, totaling around 1.4 million answered questions and around 4 million answers. This dataset offers valuable insights into consumer inquiries and the corresponding responses, facilitating research in natural language processing, question-answering systems, and e-commerce analytics. It can be combined with Amazon product review data (available here) by matching ASINs in the Q/A dataset with ASINs in the review data. The dataset is approximately 766 kB in size and is available in JSON format.

Structurally, each entry in the dataset includes the following variables:​

  • asin: The Amazon Standard Identification Number (ASIN) of the product, e.g., “B000050B6Z”.​

  • questionType: The type of question, either ‘yes/no’ or ‘open-ended’.​

  • answerType: For yes/no questions, this indicates the type of answer: ‘Y’ for yes, ‘N’ for no, or ‘?’ if the polarity of the answer could not be determined.​

  • answerTime: The raw timestamp of when the answer was provided.

  • unixTime: The answer timestamp converted to Unix time.​

  • question: The text of the question asked by the consumer.​

  • answer: The text of the answer provided.

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