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-rw-r--r--src/client/ClientRecommender.tsx39
-rw-r--r--src/client/cognitive_services/CognitiveServices.ts4
-rw-r--r--src/client/views/nodes/DocumentView.tsx26
3 files changed, 46 insertions, 23 deletions
diff --git a/src/client/ClientRecommender.tsx b/src/client/ClientRecommender.tsx
index 0e344dae9..217c89297 100644
--- a/src/client/ClientRecommender.tsx
+++ b/src/client/ClientRecommender.tsx
@@ -16,6 +16,12 @@ export interface RecommenderProps {
title: string;
}
+/**
+ * actualDoc: datadoc
+ * vectorDoc: mean vector of text
+ * score: similarity score to main doc
+ */
+
export interface RecommenderDocument {
actualDoc: Doc;
vectorDoc: number[];
@@ -28,6 +34,8 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
static Instance: ClientRecommender;
private mainDoc?: RecommenderDocument;
private docVectors: Set<RecommenderDocument> = new Set();
+ private highKP: string[] = [];
+
@observable private corr_matrix = [[0, 0], [0, 0]];
constructor(props: RecommenderProps) {
@@ -45,10 +53,6 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
ClientRecommender.Instance.corr_matrix = [[0, 0], [0, 0]];
}
- public deleteDocs() {
- console.log("deleting previews...");
- }
-
/***
* Computes the cosine similarity between two vectors in Euclidean space.
*/
@@ -82,6 +86,10 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
}
}
+ /**
+ * Returns list of {doc, similarity (to main doc)} in increasing score
+ */
+
public computeSimilarities() {
ClientRecommender.Instance.docVectors.forEach((doc: RecommenderDocument) => {
if (ClientRecommender.Instance.mainDoc) {
@@ -127,14 +135,14 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
* Uses Cognitive Services to extract keywords from a document
*/
- public async extractText(dataDoc: Doc, extDoc: Doc, mainDoc: boolean = false) {
+ public async extractText(dataDoc: Doc, extDoc: Doc, internal: boolean = true, mainDoc: boolean = false) {
let fielddata = Cast(dataDoc.data, RichTextField);
let data: string;
fielddata ? data = fielddata[ToPlainText]() : data = "";
let converter = (results: any, data: string) => {
- let keyterms = new List<string>();
- let keyterms_counted = new List<string>();
- let highKP: string[] = [""];
+ let keyterms = new List<string>(); // raw keywords
+ let keyterms_counted = new List<string>(); // keywords, where each keyword is repeated as
+ let highKP: string[] = [""]; // most frequent
let high = 0;
results.documents.forEach((doc: any) => {
let keyPhrases = doc.keyPhrases;
@@ -143,13 +151,12 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
if (frequency > high) {
high = frequency;
highKP = [kp];
-
}
else if (frequency === high) {
highKP.push(kp);
}
let words = kp.split(" "); // separates phrase into words
- words = this.removeStopWords(words);
+ words = this.removeStopWords(words); // removes stop words if they appear in phrases
words.forEach((word) => {
keyterms.push(word);
for (let i = 0; i < frequency; i++) {
@@ -158,16 +165,12 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
});
});
});
+ this.highKP = highKP;
console.log(highKP);
this.sendRequest(highKP);
return { keyterms: keyterms, keyterms_counted: keyterms_counted };
};
- await CognitiveServices.Text.Appliers.analyzer(dataDoc, extDoc, ["key words"], data, converter, mainDoc);
- }
-
- private findImportantKPs(keyterms_counted: string[], paragraph: string) {
- let imporantSet = new Set<string>();
-
+ await CognitiveServices.Text.Appliers.analyzer(dataDoc, extDoc, ["key words"], data, converter, mainDoc, internal);
}
private countFrequencies(keyphrase: string, paragraph: string) {
@@ -180,7 +183,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
// console.log("Keyphrases:", kp_array);
for (let i = 0; i <= par_length - num_keywords; i++) {
const window = data.slice(i, i + num_keywords);
- if (JSON.stringify(window) === JSON.stringify(kp_array)) {
+ if (JSON.stringify(window).toLowerCase() === JSON.stringify(kp_array).toLowerCase() || kp_array.every(val => window.includes(val))) {
frequency++;
}
}
@@ -194,7 +197,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
private async sendRequest(keywords: string[]) {
let query = "";
- keywords.forEach((kp: string) => query += kp);
+ keywords.forEach((kp: string) => query += " " + kp);
await this.arxivrequest(query);
}
diff --git a/src/client/cognitive_services/CognitiveServices.ts b/src/client/cognitive_services/CognitiveServices.ts
index eb1dd5197..8a58355a8 100644
--- a/src/client/cognitive_services/CognitiveServices.ts
+++ b/src/client/cognitive_services/CognitiveServices.ts
@@ -285,14 +285,14 @@ export namespace CognitiveServices {
});
}
- export const analyzer = async (dataDoc: Doc, target: Doc, keys: string[], data: string, converter: TextConverter, mainDoc: boolean = false) => {
+ export const analyzer = async (dataDoc: Doc, target: Doc, keys: string[], data: string, converter: TextConverter, mainDoc: boolean = false, internal: boolean = true) => {
let results = await ExecuteQuery(Service.Text, Manager, data);
console.log(results);
let keyterms = converter(results, data);
//target[keys[0]] = Docs.Get.DocumentHierarchyFromJson(results, "Key Word Analysis");
target[keys[0]] = keyterms.keyterms;
console.log("analyzed!");
- await vectorize(keyterms.keyterms_counted, dataDoc, mainDoc, data);
+ if (internal) await vectorize(keyterms.keyterms_counted, dataDoc, mainDoc, data);
};
// export async function countFrequencies()
diff --git a/src/client/views/nodes/DocumentView.tsx b/src/client/views/nodes/DocumentView.tsx
index d1e87b663..2ae71f1da 100644
--- a/src/client/views/nodes/DocumentView.tsx
+++ b/src/client/views/nodes/DocumentView.tsx
@@ -642,11 +642,23 @@ export class DocumentView extends DocComponent<DocumentViewProps, Document>(Docu
a.click();
}
});
- cm.addItem({
- description: "Recommender System",
+ let recommender_subitems: ContextMenuProps[] = [];
+
+ recommender_subitems.push({
+ description: "Internal recommendations",
event: () => this.recommender(e),
icon: "brain"
});
+
+ recommender_subitems.push({
+ description: "External recommendations",
+ event: () => this.externalRecommendation(e),
+ icon: "brain"
+ });
+
+ cm.addItem({ description: "Recommender System", subitems: recommender_subitems, icon: "brain" });
+
+
cm.addItem({ description: "Delete", event: this.deleteClicked, icon: "trash" });
type User = { email: string, userDocumentId: string };
let usersMenu: ContextMenuProps[] = [];
@@ -729,7 +741,7 @@ export class DocumentView extends DocComponent<DocumentViewProps, Document>(Docu
if (!documents.includes(dataDoc)) {
documents.push(dataDoc);
const extdoc = doc.data_ext as Doc;
- return ClientRecommender.Instance.extractText(doc, extdoc ? extdoc : doc, mainDoc);
+ return ClientRecommender.Instance.extractText(doc, extdoc ? extdoc : doc, true, mainDoc);
}
}
}));
@@ -757,6 +769,14 @@ export class DocumentView extends DocComponent<DocumentViewProps, Document>(Docu
// RecommendationsBox.Instance.displayRecommendations(e.pageX + 100, e.pageY);
}
+ externalRecommendation = async (e: React.MouseEvent) => {
+ if (!ClientRecommender.Instance) new ClientRecommender({ title: "Client Recommender" });
+ ClientRecommender.Instance.reset_docs();
+ const doc = Doc.GetDataDoc(this.props.Document);
+ const extdoc = doc.data_ext as Doc;
+ return ClientRecommender.Instance.extractText(doc, extdoc ? extdoc : doc, false);
+ }
+
onPointerEnter = (e: React.PointerEvent): void => { Doc.BrushDoc(this.props.Document); };
onPointerLeave = (e: React.PointerEvent): void => { Doc.UnBrushDoc(this.props.Document); };
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2025-10-06 23:08:22 +0000