All Yahoo Calendar Entries appear on iPhone a day earlier at 11pmpm - Ask Different
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In this paper we propose the idea of term-by-term QAC, which is a new technique inspired by predictive keyboards that suggests to the user one term at a time, instead of whole-query completions. We describe an efficient mechanism to implement this technique and an adaptation of a prior user model to evaluate the effectiveness of both standard and term-by-term QAC approaches using query log data.
Our experiments with a mobile query log from a commercial search engine show the validity of our approach according to this user model with respect to saved characters, saved terms and examination effort. Finally, a user study provides further insights about our term-by-term technique compared with standard QAC with respect to the variables analyzed in the query log-based evaluation and additional variables related to the successfulness, the speed of the interactions and the properties of the submitted queries.
Abstract Modern search engines have evolved from mere document retrieval systems to platforms that assist the users in discovering new information.
Roi Blanco's academic home page
In this context, entity recommendation systems exploit query log data to proactively provide the users with suggestions of entities people, movies, places, etc. Previous works consider the problem of ranking facts and entities related to the user's current query, or focus on specific recommendation domains requiring supervised selection and extraction of features from knowledge bases.
In this paper we propose a set of domain-agnostic methods based on nearest neighbors collaborative filtering that exploit query log data to generate entity suggestions, taking into account the user's full search session.
Our experimental results on a large dataset from a commercial search engine show that the proposed methods are able to compute relevant entity recommendations outperforming a number of baselines.
Finally, we perform an analysis on a cross-domain scenario using different entity types, and conclude that even if knowing the right target domain is important for providing effective recommendations, some inter-domain user interactions are helpful for the task at hand. The advantage is that the exact solution can be achieved, which enables us to investigate to what extent using the greedy strategy affects the performance of implicit SRD.
Table | Semantic UI
Specifically, a series of experiments are conducted to empirically compare the state-of-the-art methods with the proposed approach. The experimental results show that: Abstract Research on temporal aspects of information retrieval has recently gained considerable interest within the Information Retrieval IR community. This paper describes our efforts for building test collections for the purpose of fostering temporal IR research. In particular, we overview the test collections created at the two recent editions of Temporal Information Access Temporalia task organized at NTCIR and NTCIR, report on selected results and discuss several observations we made during the task design and implementation.
Q: Why is the number 1 not considered a prime number?
Finally, we outline further directions for constructing test collections suitable for temporal IR. We distinguish the re-finding behavior from general search, and engineer features that are effective in differentiating re-finding across the domains.
The features are then used to build machine learned models. While the accuracy of detection is We attempt to differentiate re-finding behavior when the history of a searchers interactions are not available. In this scenario we achieve an average accuracy of We also examine early detection of re-finding during a searchers session.
Finally, we investigate in which types of domains is re-finding most difficult.
It attempts to pull the latest Event from the queue and dispatches it to the correct event handler object. However, the challenge here is that the previously mentioned sentiment signals CSV file also contains timestamped sentiment signals. Hence it is necessary to "inject" the appropriate sentiment signal for a particular ticker at the correct time point in the backtest. This has been achieved by creating a new event called SentimentEvent. It stores a timestamp, a ticker and a sentiment value which can be a floating-point value, integer or a string that is sent to the Strategy object in order to generate SignalEvents.
Can be used for a generic "date-ticker-sentiment" service, often provided by many data vendors. This allows subclassing of sentiment handler objects for various vendor APIs, all shared through a common interface.
Since sentiment indicators are nearly always "timestamp-ticker-sentiment" tuples, it is useful to create a unified interface. As with most handlers it requires a handle to the events queue, a subset of tickers to act upon as well as a starting and ending date: It wraps the opening of a CSV into a pandas DataFrame along with associated ticker and date filtering: That is, the event-handler should never see a sentiment signal that is generated "in the future" by peeking too far ahead into the CSV file.
Crucially, this method actually outputs multiple SentimentEvent objects, which are all those that were generated on a particular day: It involves modifying the event dispatcher to handle the addition of SentimentEvent objects that must be dispatched to an appropriate Strategy object.
The first of these checks whether this is a strategy that contains a SentimentHandler or not. If it does, then all SentimentEvent objects for a particular day, referenced in the Sentdex sentiment file, are created. Further down the event handler such events are sent to the Strategy object, which will then act upon them to generate signals: These changes are now in the latest version found on Githubso if you wish to replicate these strategies, make sure to update your local QSTrader version to the latest copy.
Sentiment Analysis Strategy Code The full code listings for this strategy and backtest are presented at the end of the article.Math Antics - Negative Numbers
The above modifications to QSTrader provide the necessary structure to run a sentiment analysis strategy. However it remains to be shown how the above entry and exit rules are actually implemented.
As it turns out the majority of the "hard work" has been done in the above modules. The strategy implementation itself is relatively straightforward. As always the first task is to import the necessary libraries. Both of these are specified later in the backtest code. In addition a base quantity of shares is required for trading. In order to keep the strategy relatively straightforward the position sizing solely buys and sells such a base quantity for each ticker at any time point in the strategy.
That is, there is no dynamic adjustment of position sizes or percentage allocation to any ticker. In a production strategy this would be one of the first parts to optimise. Since this position sizing code is likely to distract from the main "sentiment" aspect of the strategy, it was decided that it be kept simple for this article. In every strategy presented thus far the first line in this method always checks what the event type is if event.
This provides greater flexibility in AbstractStrategy subclasses, since they can respond to arbitrary events, not just those based around asset pricing data. Once the event has been confirmed as a SentimentEvent the code checks whether that particular ticker is already being traded.
- Do whole numbers include negative integers?
- Are negative numbers whole numbers?
- List all numbers from the given set that are?
If not, it checks whether the sentiment exceeds the sentiment integer entry threshold and then creates a long of the base quantity of shares. If it is already trading this ticker and the current sentiment threshold is below the provided exit threshold, then it closes the position.
Hence the strategy presented below only goes long. It is a straightforward matter to extend this to short trading.
Example code for shorting has been presented in other trading strategies to date.